@article{Guo2025DeepEMs25_DEEPMD,
  title        = {DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials},
  author       = {Guo, Ming-Yu and Yan, Yun-Fan and Chen, Pin and Zhang, Wei-Xiong},
  journal      = {npj Computational Materials},
  volume       = {11},
  number       = {1},
  pages        = {246},
  year         = {2025},
  publisher    = {Nature Publishing Group},
  doi          = {10.1038/s41524-025-01739-7},
  url          = {https://www.nature.com/articles/s41524-025-01739-7}
}


@Article{Infuso2025DeepPotentialDrivenMolecularDynamics_DEEPMD,
  author       = {Infuso, Maxime and Del Fré, Samuel and Alou Angulo, Gilberto A. and Bertin, Mathieu and Fillion, Jean-Hugues and Rivero Santamaría, Alejandro and Monnerville, Maurice},
  title        = {Deep potential-driven molecular dynamics of CO ice analogs: Investigating desorption following vibrational excitation},
  journal      = {The Journal of Chemical Physics},
  volume       = {163},
  number       = {8},
  pages        = {084303},
  year         = {2025},
  doi          = {10.1063/5.0285103},
  url          = {https://doi.org/10.1063/5.0285103}
}

@Article{Chandran2024ComparativeAnalysisTernaryTiAlNb_DEEPMD,
  author       = {Chandran, Anju and Santhosh, Archa and Pistidda, Claudio and Jerabek, Paul and Aydin, Roland C. and Cyron, Christian J.},
  title        = {Comparative analysis of ternary {\( \text{Ti–Al–Nb} \)} interatomic potentials: moment tensor vs. deep learning approaches},
  journal      = {Frontiers in Materials},
  volume       = {11},
  year         = {2024},
  doi          = {10.3389/fmats.2024.1466793},
  url          = {https://doi.org/10.3389/fmats.2024.1466793}
}

@Article{Gu2024DeepLearningTightBinding_DEEPMD,
  author       = {Gu, Qiangqiang and Zhouyin, Zhanghao and Pandey, Shishir Kumar and Zhang, Peng and Zhang, Linfeng and E, Weinan},
  title        = {Deep learning tight-binding approach for large‐scale electronic simulations at finite temperatures with ab initio accuracy},
  journal      = {Nature Communications},
  volume       = {15},
  number       = {1},
  pages        = {6772},
  year         = {2024},
  doi          = {10.1038/s41467-024-51006-4},
  url          = {https://doi.org/10.1038/s41467-024-51006-4}
}

@Article{Zhu_npjDrugDiscov_2025_v2_p1,
    author =   {Hui Zhu and Xuelian Li and Baoquan Chen and Niu Huang},
    title =    {{Augmented BindingNet dataset for enhanced ligand binding pose
             predictions using deep learning}},
    journal =  {npj Drug Discov,},
    year =     2025,
    volume =   2,
    number =   1,
    pages =    1,
    doi =      {10.1038/s44386-024-00003-0},
}

@Article{Dai_AdvPhysRes_2025_v4,
    author =   {Yin Dai and Menghao Wu},
    title =    {{Giant Inverse Barocaloric Effect of Ferroelectric Salts Driven by
             Negative Thermal Expansion}},
    journal =  {Adv. Phys. Res.},
    year =     2025,
    volume =   4,
    number =   4,
    doi =      {10.1002/apxr.202400125},
    abstract = {AbstractRefrigeration technologies based on the barocaloric effect
             have garnered significant attention, while their potential
             applications are limited by the poor performance of current materials.
             Here it is proposed that ferroelectric ionic salts with
             covalent{-}like bondings like LiI may become ideal candidates. The
             pressure{-}induced phase transition between two phases with distinct
             densities and entropies leads to tremendous negative thermal expansion
             (with volume reduced by 13{\%}) and inverse barocaloric effect, which
             are facilitated by the low transition barriers due to the long{-}range
             Coulomb interaction. Using ab initio{-}based training database, we
             trained the machine learning potential of LiI based on a deep neutral
             network{-}based mode, and this simulations of its barocaloric effect
             reveal a high entropy change and thermal conductivity, and in
             particular, the estimated adiabatic temperature change, pressure
             sensitivity and relative cooling power are all unprecedented. This
             prediction provides a high{-}performance barocaloric mechanism for
             practical applications and also expands the scope of barocaloric
             materials to simple and facile binary salts.},
}

@Article{Zeng_JChemInfModel_2025_v65_p3154,
    author =   {Jinzhe Zeng and Timothy J. Giese and Duo Zhang and Han Wang and Darrin
             M. York},
    title =    {{DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network
             Potentials}},
    journal =  {J. Chem. Inf. Model.},
    year =     2025,
    volume =   65,
    number =   7,
    pages =    {3154--3160},
    doi =      {10.1021/acs.jcim.4c02441},
    abstract = {Machine learning potentials (MLPs) have revolutionized molecular
             simulation by providing efficient and accurate models for predicting
             atomic interactions. MLPs continue to advance and have had profound
             impact in applications that include drug discovery, enzyme catalysis,
             and materials design. The current landscape of MLP software presents
             challenges due to the limited interoperability between packages, which
             can lead to inconsistent benchmarking practices and necessitates
             separate interfaces with molecular dynamics (MD) software. To address
             these issues, we present DeePMD-GNN, a plugin for the DeePMD-kit
             framework that extends its capabilities to support external graph
             neural network (GNN) potentials.DeePMD-GNN enables the seamless
             integration of popular GNN-based models, such as NequIP and MACE,
             within the DeePMD-kit ecosystem. Furthermore, the new software
             infrastructure allows GNN models to be used within combined quantum
             mechanical/molecular mechanical (QM/MM) applications using the range
             corrected {\ensuremath{\Delta}}MLP formalism.We demonstrate the
             application of DeePMD-GNN by performing benchmark calculations of
             NequIP, MACE, and DPA-2 models developed under consistent training
             conditions to ensure fair comparison.},
}

@Article{Shi_arXiv_2025_p2503.06039,
    author =   {Guoyong Shi and Fenglin Deng and Ri He and Dachuan Chen and Xuejiao
             Chen and Peiheng Jiang and Zhicheng Zhong},
    title =    {{Temperature-driven structural phase transitions in SmNiO{\$}{\_}3{\$}:
             insights from deep potential molecular dynamics simulations}},
    journal =  {arXiv},
    year =     2025,
    pages =    {2503.06039},
    doi =      {10.48550/arXiv.2503.06039},
    abstract = {The metal-insulator transition (MIT) in rare-earth nickelates
             exemplifies the intricate interplay between electronic correlations
             and lattice dynamics in quantum materials. This work focuses on
             SmNiO{\$}{\_}3{\$} as a prototypical system, employing molecular
             dynamics simulations based on a {''}hidden{''} magnetic potential
             model. Our simulations reveal two key findings. First, the structural
             phase transition in SmNiO{\$}{\_}3{\$} is intrinsically temperature-
             driven and occurs spontaneously via collective lattice distortions.
             Moreover, systematic high-pressure simulations demonstrate a distinct
             pressure dependence of the transition temperature, which decreases
             monotonically with increasing external hydrostatic pressure. These
             results provide atomistic insights into the cooperative mechanisms
             underlying the MIT and the interplay between structural distortions
             and electron correlation effects. The computational approach developed
             herein offers a generalizable framework for investigating complex
             phase transitions in correlated quantum materials.},
}

@Article{Pei_arXiv_2025_p2406.05797,
    author =   {Qizhi Pei and Rui Yan and Kaiyuan Gao and Jinhua Zhu and Lijun Wu},
    title =    {{3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text
             Modeling}},
    journal =  {arXiv},
    year =     2025,
    pages =    {2406.05797},
    doi =      {10.48550/arXiv.2406.05797},
    abstract = {The integration of molecular and natural language representations has
             emerged as a focal point in molecular science, with recent
             advancements in Language Models (LMs) demonstrating significant
             potential for comprehensive modeling of both domains. However,
             existing approaches face notable limitations, particularly in their
             neglect of three-dimensional (3D) information, which is crucial for
             understanding molecular structures and functions. While some efforts
             have been made to incorporate 3D molecular information into LMs using
             external structure encoding modules, significant difficulties remain,
             such as insufficient interaction across modalities in pre-training and
             challenges in modality alignment. To address the limitations, we
             propose {\textbackslash}textbf{\{}3D-MolT5{\}}, a unified framework
             designed to model molecule in both sequence and 3D structure spaces.
             The key innovation of our approach lies in mapping fine-grained 3D
             substructure representations into a specialized 3D token vocabulary.
             This methodology facilitates the seamless integration of sequence and
             structure representations in a tokenized format, enabling 3D-MolT5 to
             encode molecular sequences, molecular structures, and text sequences
             within a unified architecture. Leveraging this tokenized input
             strategy, we build a foundation model that unifies the sequence and
             structure data formats. We then conduct joint pre-training with multi-
             task objectives to enhance the model's comprehension of these diverse
             modalities within a shared representation space. Thus, our approach
             significantly improves cross-modal interaction and alignment,
             addressing key challenges in previous work. Further instruction tuning
             demonstrated that our 3D-MolT5 has strong generalization ability and
             surpasses existing methods with superior performance in multiple
             downstream tasks. Our code is available at
             https://github.com/QizhiPei/3D-MolT5.},
}

@Article{Chen_arXiv_2025_p2506.00880,
    author =   {Zhuo Chen and Yizhen Zheng and Huan Yee Koh and Hongxin Xiang and
             Linjiang Chen and Wenjie Du and Yang Wang},
    title =    {{ModuLM: Enabling Modular and Multimodal Molecular Relational Learning
             with Large Language Models}},
    journal =  {arXiv},
    year =     2025,
    pages =    {2506.00880},
    doi =      {10.48550/arXiv.2506.00880},
    abstract = {Molecular Relational Learning (MRL) aims to understand interactions
             between molecular pairs, playing a critical role in advancing
             biochemical research. With the recent development of large language
             models (LLMs), a growing number of studies have explored the
             integration of MRL with LLMs and achieved promising results. However,
             the increasing availability of diverse LLMs and molecular structure
             encoders has significantly expanded the model space, presenting major
             challenges for benchmarking. Currently, there is no LLM framework that
             supports both flexible molecular input formats and dynamic
             architectural switching. To address these challenges, reduce redundant
             coding, and ensure fair model comparison, we propose ModuLM, a
             framework designed to support flexible LLM-based model construction
             and diverse molecular representations. ModuLM provides a rich suite of
             modular components, including 8 types of 2D molecular graph encoders,
             11 types of 3D molecular conformation encoders, 7 types of interaction
             layers, and 7 mainstream LLM backbones. Owing to its highly flexible
             model assembly mechanism, ModuLM enables the dynamic construction of
             over 50,000 distinct model configurations. In addition, we provide
             comprehensive results to demonstrate the effectiveness of ModuLM in
             supporting LLM-based MRL tasks.},
}

@Article{Zeng_JChemTheoryComput_2025_v21_p4375,
    author =   {Jinzhe Zeng and Duo Zhang and Anyang Peng and Xiangyu Zhang and Sensen
             He and Yan Wang and Xinzijian Liu and Hangrui Bi and Yifan Li and Chun
             Cai and Chengqian Zhang and Yiming Du and Jia-Xin Zhu and Pinghui Mo
             and Zhengtao Huang and Qiyu Zeng and Shaochen Shi and Xuejian Qin and
             Zhaoxi Yu and Chenxing Luo and Ye Ding and Yun-Pei Liu and Ruosong Shi
             and Zhenyu Wang and Sigbj{\o}rn L{\o}land Bore and Junhan Chang and
             Zhe Deng and Zhaohan Ding and Siyuan Han and Wanrun Jiang and Guolin
             Ke and Zhaoqing Liu and Denghui Lu and Koki Muraoka and Hananeh Oliaei
             and Anurag Kumar Singh and Haohui Que and Weihong Xu and Zhangmancang
             Xu and Yong-Bin Zhuang and Jiayu Dai and Timothy J. Giese and Weile
             Jia and Ben Xu and Darrin M. York and Linfeng Zhang and Han Wang},
    title =    {{DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning
             Potentials}},
    journal =  {J. Chem. Theory Comput.},
    year =     2025,
    volume =   21,
    number =   9,
    pages =    {4375--4385},
    doi =      {10.1021/acs.jctc.5c00340},
    abstract = {In recent years, machine learning potentials (MLPs) have become
             indispensable tools in physics, chemistry, and materials science,
             driving the development of software packages for molecular dynamics
             (MD) simulations and related applications. These packages, typically
             built on specific machine learning frameworks, such as TensorFlow,
             PyTorch, or JAX, face integration challenges when advanced
             applications demand communication across different frameworks. The
             previous TensorFlow-based implementation of the DeePMD-kit exemplified
             these limitations. In this work, we introduce DeePMD-kit version 3, a
             significant update featuring a multibackend framework that supports
             TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate
             the versatility of this architecture through the integration of other
             MLP packages and of differentiable molecular force fields. This
             architecture allows seamless back-end switching with minimal
             modifications, enabling users and developers to integrate DeePMD-kit
             with other packages using different machine learning frameworks. This
             innovation facilitates the development of more complex and
             interoperable workflows, paving the way for broader applications of
             MLPs in scientific research.},
}

@Article{Luo_2DMater_2025_v12_p15022,
    author =   {Jiangbo Luo and Xudong Zhu and Xu Lian and Yuntian Zheng and Reshmi
             Thottathil and Wei Chen and Song Liu and A Ariando and Junxiong Hu},
    title =    {{Tuning oxygen vacancies in complex oxides using 2D layered materials}},
    journal =  {2D Mater.},
    year =     2025,
    volume =   12,
    number =   1,
    pages =    15022,
    doi =      {10.1088/2053-1583/ada041},
    abstract = {Abstract                The hybrid interface between 2D materials and
             complex oxides offers a rich platform to explore fascinating physical
             phenomena like helical edge states, broken-symmetry phases, and giant
             magnetoresistance. While current research primarily focuses on the
             influence of complex oxides on layered 2D materials, the
             reverse{\textemdash}how layered 2D materials affect complex
             oxides{\textemdash}remains largely unexplored. Here, we examined the
             impact of graphene layers on the formation of oxygen vacancies in
             SrTiO3 (STO) during high-temperature annealing. Our findings,
             supported by Raman spectroscopy and x-ray photoelectron spectroscopy,
             indicate that increasing the number of graphene layers progressively
             leads to a reduced oxygen vacancy content in STO, demonstrating the
             efficacy of graphene in modulating oxygen vacancy formation in bulk
             STO. Additionally, using photoluminescence, we showed that graphene
             layers can tune the in-gap states induced by oxygen vacancies in STO.
             Our first principal calculations further revealed that graphene layers
             increase the energy barrier for the outward diffusion of oxygen atoms,
             thereby inhibiting the formation of oxygen vacancies in STO. These
             results highlight a new route for tailoring the physical properties of
             complex oxides by engineering the interface with layered 2D materials.},
}

@Article{Jin_JPhysCondensMatterInstPhysJ_2021_v33_p325503,
    author =   {Gan Jin and Daye Zheng and Lixin He},
    title =    {{Calculation of Berry curvature using non-orthogonal atomic orbitals}},
    journal =  {J. Phys., Condens. Matter: Inst. Phys. J.},
    year =     2021,
    volume =   33,
    number =   32,
    pages =    325503,
    doi =      {10.1088/1361-648X/ac05e5},
    abstract = {We present a derivation of the full formula to calculate the Berry
             curvature on non-orthogonal numerical atomic orbital (NAO) bases.
             Because usually, the number of NAOs is larger than that of the Wannier
             bases, we use a orbital contraction method to reduce the basis sizes,
             which can greatly improve the calculation efficiency without
             significantly reducing the calculation accuracy. We benchmark the
             formula by calculating the Berry curvature of ferroelectric BaTiO3and
             bcc Fe, as well as the anomalous Hall conductivity for Fe. The results
             are in excellent agreement with the finite-difference and previous
             results in the literature. We find that there are corrections terms to
             the Kubo formula of the Berry curvature. For the full NAO base, the
             differences between the two methods are negligibly small, but for the
             reduced bases sets, the correction terms become larger, which may not
             be neglected in some cases. The formula developed in this work can
             readily be applied to the non-orthogonal generalized Wannier
             functions.},
}

@Article{Chen_PhysRevB_2009_v80_p165121,
    author =   {Mohan Chen and Wei Fang and G.-Z. Sun and G.-C. Guo and Lixin He},
    title =    {{Method to construct transferable minimal basis sets forab
             initiocalculations}},
    journal =  {Phys. Rev. B},
    year =     2009,
    volume =   80,
    number =   16,
    pages =    165121,
    doi =      {10.1103/PhysRevB.80.165121},
    abstract = {We propose a scheme to construct transferable minimal basis of
             localized orbitals for ab initio calculations. We first extract a set
             of highly localized Wannier-type orbitals from the reference systems.
             For each orbital, we decompose it to a pseudoatomic orbital, augmented
             by small local functions centered at its nearest-neighbor atoms. When
             applied for a real system, the center of each local function moves
             with its associated atoms, without changing its shape and amplitude.
             We have done intensive tests of this scheme for
             III{\textbackslash}char21{\{}{\}}V and group IV semiconductors and
             find the modified orbitals have very good transferability while still
             keep the basis size minimal. This work discusses why Wannier and
             Wannier-type functions are not transferable as one may expect.},
}

@Article{Li_PhysRevB_2023_v107_p35433,
    author =   {Pengfei Li and Rong Shi and Peize Lin and Xinguo Ren},
    title =    {{First-principles calculations of plasmon excitations in graphene,
             silicene, and germanene}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   107,
    number =   3,
    pages =    35433,
    doi =      {10.1103/PhysRevB.107.035433},
    abstract = {Plasmon excitations in graphene, silicene and germanene are studied
             using linear-response time-dependent density functional theory within
             the random phase approximation (RPA). Here, we examine both the
             plasmon dispersion behavior and lifetime of extrinsic and intrinsic
             plasmons for these three materials. For extrinsic plasmons, we found
             that their properties are closely related to Landau damping. In the
             region without single-particle excitation (SPE), the plasmon
             dispersion shows a {\ensuremath{\sqrt{}}} q behavior and the lifetime
             is in{fi}nite at the RPA level, while in the single-particle
             excitation region, the plasmon dispersion shows a quasilinear behavior
             and the lifetime is {fi}nite. Moreover, for intrinsic plasmons, unlike
             graphene, the plasmon dispersion behavior of silicene and germanene
             exhibits a two-peak structure, which can be attributed to the complex
             and hybridized band structure of these two materials.},
}

@Article{Sun_PhysRevB_2023_v108_p75158,
    author =   {Liang Sun and Yuanbo Li and Mohan Chen},
    title =    {{Truncated nonlocal kinetic energy density functionals for simple
             metals and silicon}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   108,
    number =   7,
    pages =    75158,
    doi =      {10.1103/PhysRevB.108.075158},
    abstract = {Adopting an accurate kinetic energy density functional (KEDF) to
             characterize the noninteracting kinetic energy within the framework of
             orbital-free density functional theory (OFDFT) is challenging. We
             propose a new form of the non-local KEDF with a real-space truncation
             cutoff that satisfies the uniform electron gas limit and design KEDFs
             for simple metals and silicon. The new KEDFs are obtained by
             minimizing a residual function, which contains the differences in the
             total energy and charge density of several representative systems with
             respect to the Kohn-Sham DFT results. By systematically testing
             different cutoffs of the new KEDFs, we find that the cutoff plays a
             crucial role in determining the properties of metallic Al and
             semiconductor Si systems. We conclude that the new KEDF with a
             sufficiently long cutoff performs even better than some representative
             non-local KEDFs in some aspects, which sheds new light on optimizing
             the KEDFs in OFDFT to achieve better accuracy.},
}

@Article{Bakhsh_BeilsteinJNanotechnol_2024_v15_p310,
    author =   {Sunila Bakhsh and Muhammad Khalid and Sameen Aslam and Muhammad Sohail
             and Muhammad Aamir Iqbal and Mujtaba Ikram and Kareem Morsy},
    title =    {{Investigating structural and electronic properties of neutral zinc
             clusters: a G0W0 and G0W0{\CYRG}0(1) benchmark}},
    journal =  {Beilstein J. Nanotechnol.},
    year =     2024,
    volume =   15,
    pages =    {310--316},
    doi =      {10.3762/bjnano.15.28},
    abstract = {The structural and electronic properties of zinc clusters (Znn) for a
             size range of n = 2-15 are studied using density functional theory.
             The particle swarm optimization algorithm is employed to search the
             structure and to determine the ground-state structure of the neutral
             Zn clusters. The structural motifs are optimized using the density
             functional theory approach to ensure that the structures are fully
             relaxed. Results are compared with the literature to validate the
             accuracy of the prediction method. The binding energy per cluster is
             obtained and compared with the reported literature to study the
             stability of these structures. We further assess the electronic
             properties, including the ionization potential, using the all-electron
             FHI-aims code employing G0W0 calculations, and the G0W0{\CYRG}0(1)
             correction for a few smaller clusters, which provides a better
             estimation of the ionization potential compared to other methods.},
}

@Article{Zhang_AdvMaterDeerfieldBeachFla_2024_pe2411137,
    author =   {Xiaoqian Zhang and Qiangsheng Lu and Zhen-Xiong Shen and Wei Niu and
             Xiangrui Liu and Jiahua Lu and Wenting Lin and Lulu Han and Yakui Weng
             and Tianhao Shao and Pengfei Yan and Quan Ren and Huayao Li and Tay-
             Rong Chang and David J. Singh and Lixin He and Liang He and Chang Liu
             and Guang Bian and Lin Miao and Yongbing Xu},
    title =    {{Substantially Enhanced Spin Polarization in Epitaxial CrTe2 Quantum
             Films}},
    journal =  {Adv. Mater. (Deerfield Beach Fla,)},
    year =     2024,
    pages =    {e2411137},
    doi =      {10.1002/adma.202411137},
    abstract = {2D van der Waals (vdW) magnets, which extend to the monolayer (ML)
             limit, are rapidly gaining prominence in logic applications for low-
             power electronics. To improve the performance of spintronic devices,
             such as vdW magnetic tunnel junctions, a large effective spin
             polarization of valence electrons is highly desired. Despite its
             considerable significance, direct probe of spin polarization in these
             2D magnets has not been extensively explored. Here, using 2D vdW
             ferromagnet of CrTe2 as a prototype, the spin degrees of freedom in
             the thin films are directly probed using Mott polarimetry. The
             electronic band of 50 ML CrTe2 thin film, spanning the Brillouin zone,
             exhibits pronounced spin-splitting with polarization peaking at
             7.9{\%} along the out-of-plane direction. Surprisingly, atomic-layer-
             dependent spin-resolved measurements show a significantly enhanced
             spin polarization in a 3 ML CrTe2 film, achieving 23.4{\%}
             polarization even in the absence of an external magnetic field. The
             demonstrated correlation between spin polarization and film thickness
             highlights the pivotal influence of perpendicular magnetic anisotropy,
             interlayer interactions, and itinerant behavior on these properties,
             as corroborated by theoretical analysis. This groundbreaking
             experimental verification of intrinsic effective spin polarization in
             CrTe2 ultrathin films marks a significant advance in establishing 2D
             ferromagnetic atomic layers as a promising platform for innovative
             vdW-based spintronic devices.},
}

@Article{Zhang_npjComputMater_2024_v10_p293,
    author =   {Duo Zhang and Xinzijian Liu and Xiangyu Zhang and Chengqian Zhang and
             Chun Cai and Hangrui Bi and Yiming Du and Xuejian Qin and Anyang Peng
             and Jiameng Huang and Bowen Li and Yifan Shan and Jinzhe Zeng and
             Yuzhi Zhang and Siyuan Liu and Yifan Li and Junhan Chang and Xinyan
             Wang and Shuo Zhou and Jianchuan Liu and Xiaoshan Luo and Zhenyu Wang
             and Wanrun Jiang and Jing Wu and Yudi Yang and Jiyuan Yang and Manyi
             Yang and Fu-Qiang Gong and Linshuang Zhang and Mengchao Shi and Fu-Zhi
             Dai and Darrin M. York and Shi Liu and Tong Zhu and Zhicheng Zhong and
             Jian Lv and Jun Cheng and Weile Jia and Mohan Chen and Guolin Ke and
             Weinan E and Linfeng Zhang and Han Wang},
    title =    {{DPA-2: a large atomic model as a multi-task learner}},
    journal =  {npj Comput. Mater},
    year =     2024,
    volume =   10,
    number =   1,
    pages =    293,
    doi =      {10.1038/s41524-024-01493-2},
}

@Article{Sun_NanoLett_2024_v24_p16283,
    author =   {Dongdong Sun and Xudong Zhu and Shaochuan Chen and Haotian Fang and
             Guixu Zhu and Gongpeng Lan and Lixin He and Yuanyuan Shi},
    title =    {{Uniformity, Linearity, and Symmetry Enhancement in TiOx/MoS2-xOx Based
             Analog RRAM via S-Vacancy Confined Nanofilament}},
    journal =  {Nano Lett.},
    year =     2024,
    volume =   24,
    number =   51,
    pages =    {16283--16292},
    doi =      {10.1021/acs.nanolett.4c04434},
    abstract = {Due to the stochastic formation of conductive filaments (CFs), analog
             resistive random-access memory (RRAM) struggles to simultaneously
             achieve low variability, high linearity, and symmetry in conductance
             tuning, thus complicating on-chip training and limiting versatility of
             RRAM based computing-in-memory (CIM) chips. In this study, we present
             a simple and effective approach using monolayer (ML) MoS2 as
             interlayer to control the CFs formation in TiOx switching layer. The
             limited S-vacancies (Sv) in MoS2-xOx interlayer can further confine
             the position, size, and quantity of CFs, resulting in a highly uniform
             and symmetrical switching behavior. The set and reset voltages (Vset
             and Vreset) in TiOx/MoS2-xOx based RRAM are symmetric, with cycle-to-
             cycle variations of 1.28{\%} and 1.7{\%}, respectively. Moreover, high
             conductance tuning linearity and 64-level switching capabilities are
             achieved, which facilitate high accuracy (93.02{\%}) on-chip training.
             This method mitigates the device nonidealities of analog RRAM through
             Sv confined CFs, accelerating the development of RRAM based CIM chips.},
}

@Article{Zhang_PhysRevB_2024_v110_p224419,
    author =   {Tinghai Zhang and Yantao Cao and Bo Zhang and Hanjie Guo and Liang
             Qiao and Fashen Li and Zhiwei Li},
    title =    {{Short-range order and strong interplay between local and itinerant
             magnetism in  GeFe3N}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   110,
    number =   22,
    pages =    224419,
    doi =      {10.1103/PhysRevB.110.224419},
}

@Article{Yang_PhysRevB_2024_v110_p235410,
    author =   {Shengguo Yang and Jiaxin Chen and Chao-Fei Liu and Mingxing Chen},
    title =    {{Evolution of flat bands in  MoSe2/WSe2  moir{\'e} lattices: A study
             combining machine learning and band unfolding methods}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   110,
    number =   23,
    pages =    235410,
    doi =      {10.1103/PhysRevB.110.235410},
    abstract = {Moir{\textbackslash}'e lattices have served as the ideal quantum
             simulation platform for exploring novel physics due to the flat
             electronic bands resulting from the long wavelength
             moir{\textbackslash}'e potentials. However, the large sizes of this
             type of system challenge the first-principles methods for full
             calculations of their electronic structures, thus bringing
             difficulties in understanding the nature and evolution of the flat
             bands. In this study, we investigate the electronic structures of
             moir{\textbackslash}'e patterns of MoSe{\$}{\_}2{\$}/WSe{\$}{\_}2{\$}
             by combining ab initio and machine learning methods. We find that a
             flat band with a bandwidth of about 5 meV emerges below the valence
             band edge at the K point for the H-stacking at a twist angle of
             3.89{\$}{\textasciicircum}{\{}{\textbackslash}circ{\}}{\$} without
             spin-orbit coupling effect. Then, it shifts dramatically as the twist
             angle decreases and becomes about 20 meV higher than the valence band
             maximum for the twist angle of
             3.15{\$}{\textasciicircum}{\{}{\textbackslash}circ{\}}{\$}. Multiple
             ultra-flat bands emerge as the twist angle is reduced to
             1.7{\$}{\textasciicircum}{\{}{\textbackslash}circ{\}}{\$}. The spin-
             orbit coupling leads to a giant spin splitting comparable to that
             observed in the untwisted system (about 0.45 eV) and is nearly
             independent of twisting and stacking. As a result, the K-valley flat
             band remains the valence band maximum with the inclusion of spin-orbit
             coupling. Band unfolding reveals that the ultra-flat bands formed by
             the {\$}{\textbackslash}Gamma{\$} and K valleys show distinct
             behaviors. The {\$}{\textbackslash}Gamma{\$}-valley flat bands are
             sensitive to the interlayer coupling, thus experiencing dramatic
             changes as the twist angle decreases. In contrast, the K-valley flat
             band, which shows a weak dependence on the interlayer coupling, is
             mainly modulated by structural reconstruction. Therefore, a relatively
             small angle
             (2.13{\$}{\textasciicircum}{\{}{\textbackslash}circ{\}}{\$}) is
             required to generate the K-valley flat band, which experiences a
             transition from the honeycomb to the triangular lattice as the twist
             angle decreases.},
}

@Article{Tang_NatCommun_2024_v15_p8815,
    author =   {Zechen Tang and He Li and Peize Lin and Xiaoxun Gong and Gan Jin and
             Lixin He and Hong Jiang and Xinguo Ren and Wenhui Duan and Yong Xu},
    title =    {{A deep equivariant neural network approach for efficient hybrid
             density functional calculations}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    8815,
    doi =      {10.1038/s41467-024-53028-4},
    abstract = {Hybrid density functional calculations are essential for accurate
             description of electronic structure, yet their widespread use is
             restricted by the substantial computational cost. Here we develop
             DeepH-hybrid, a deep equivariant neural network method for learning
             the hybrid-functional Hamiltonian as a function of material structure,
             which circumvents the time-consuming self-consistent field iterations
             and enables the study of large-scale materials with hybrid-functional
             accuracy. Our extensive experiments demonstrate good reliability as
             well as effective transferability and efficiency of the method. As a
             notable application, DeepH-hybrid is applied to study large-supercell
             Moir{\'e}-twisted materials, offering the first case study on how the
             inclusion of exact exchange affects flat bands in magic-angle twisted
             bilayer graphene. The work generalizes deep-learning electronic
             structure methods to beyond conventional density functional theory,
             facilitating the development of deep-learning-based ab initio methods.},
}
@Article{Gu_NatCommun_2024_v15_p6772,
    author =   {Qiangqiang Gu and Zhanghao Zhouyin and Shishir Kumar Pandey and Peng
             Zhang and Linfeng Zhang and Weinan E},
    title =    {{Deep learning tight-binding approach for large-scale electronic
             simulations at finite temperatures with ab initio accuracy}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    6772,
    doi =      {10.1038/s41467-024-51006-4},
    abstract = {Simulating electronic behavior in materials and devices with realistic
             large system sizes remains a formidable task within the ab initio
             framework due to its computational intensity. Here we show DeePTB, an
             efficient deep learning-based tight-binding approach with ab initio
             accuracy to address this issue. By training on structural data and
             corresponding ab initio eigenvalues, the DeePTB model can efficiently
             predict tight-binding Hamiltonians for unseen structures, enabling
             efficient simulations of large-size systems under external
             perturbations such as finite temperatures and strain. This capability
             is vital for semiconductor band gap engineering and materials design.
             When combined with molecular dynamics, DeePTB facilitates efficient
             and accurate finite-temperature simulations of both atomic and
             electronic behavior simultaneously. This is demonstrated by computing
             the temperature-dependent electronic properties of a gallium phosphide
             system with 106 atoms. The availability of DeePTB bridges the gap
             between accuracy and scalability in electronic simulations,
             potentially advancing materials science and related fields by enabling
             large-scale electronic structure calculations.},
}
@Article{Liu_ActaMater_2024_v279_p120294,
    author =   {Yu Liu and Yin Zhang and Namin Xiao and Xingwu Li and Fu-Zhi Dai and
             Mohan Chen},
    title =    {{Investigating interfacial segregation of {\ensuremath{\Omega}}/Al in
             Al{\textendash}Cu alloys: A comprehensive study using density
             functional theory and machine learning}},
    journal =  {Acta Mater.},
    year =     2024,
    volume =   279,
    pages =    120294,
    doi =      {10.1016/j.actamat.2024.120294},
}
@Article{Shi_PhysRevB_2024_v109_p35103,
    author =   {Rong Shi and Peize Lin and Min-Ye Zhang and Lixin He and Xinguo Ren},
    title =    {{Subquadratic-scaling real-space random phase approximation correlation
             energy calculations for periodic systems with numerical atomic
             orbitals}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   3,
    pages =    35103,
    doi =      {10.1103/PhysRevB.109.035103},
    abstract = {The random phase approximation (RPA) as formulated as an orbital-
             dependent, fifth-rung functional within the density functional theory
             (DFT) framework offers a promising approach for calculating the
             ground-state energies and the derived properties of real materials.
             Its widespread use to large-size, complex materials is however impeded
             by the significantly increased computational cost, compared to lower-
             rung functionals. The standard implementation exhibits an
             {\$}{\textbackslash}mathcal{\{}O{\}}(N{\textasciicircum}4){\$}-scaling
             behavior with respect to system size {\$}N{\$}. In this work, we
             develop a low-scaling RPA algorithm for periodic systems, based on the
             numerical atomic orbital (NAO) basis-set framework and a localized
             variant of the resolution of identity (RI) approximation. The rate-
             determining step for RPA calculations -- the evaluation of non-
             interacting response function matrix, is reduced from
             {\$}{\textbackslash}mathcal{\{}O{\}}(N{\textasciicircum}4){\$} to
             {\$}{\textbackslash}mathcal{\{}O{\}}(N{\textasciicircum}2){\$} by just
             exploiting the sparsity of the RI expansion coefficients, resultant
             from localized RI (LRI) scheme and the strict locality of NAOs. The
             computational cost of this step can be further reduced to linear
             scaling if the decay behavior of the Green's function in real space
             can be further taken into account. Benchmark calculations against
             existing {\$}{\textbackslash}textbf k{\$}-space based implementation
             confirms the validity and high numerical precision of the present
             algorithm and implementation. The new RPA algorithm allows us to
             readily handle three-dimensional, closely-packed solid state materials
             with over 1000 atoms. The algorithm and numerical techniques developed
             in this work also have implications for developing low-scaling
             algorithms for other correlated methods to be applicable to large-
             scale extended materials.},
}
@Article{Ma_PhysRevB_2024_v110_p85113,
    author =   {Cheng Ma and Min Chen and Yu Xie and Qiang Xu and Wenhui Mi and
             Yanchao Wang and Yanming Ma},
    title =    {{Nonlocal free-energy density functional for a broad range of warm
             dense matter simulations}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   110,
    number =   8,
    pages =    85113,
    doi =      {10.1103/PhysRevB.110.085113},
}
@Article{Ji_PhysRevRes_2024_v6_p33172,
    author =   {Yuyang Ji and Peize Lin and Xinguo Ren and Lixin He},
    title =    {{Geometric and electronic structures of  Cs2BB{'}X6  double
             perovskites: The importance of exact exchange}},
    journal =  {Phys, Rev, Res.},
    year =     2024,
    volume =   6,
    number =   3,
    pages =    33172,
    doi =      {10.1103/PhysRevResearch.6.033172},
    abstract = {A widely adopted computational protocol in contemporary materials
             research is to first relax materials' geometries using semilocal
             density functional approximations (DFA), and then determining their
             electronic band structures using the more expensive hybrid
             functionals. This procedure often works well, as the popular semilocal
             DFAs, such as the Perdew-Burke-Ernzerhof (PBE) generalized gradient
             approximation, yield rather good geometries for a wide range of
             materials. However, here we show that, for some of the lead-free
             halide double perovskites (HDPs) Cs2BB{'}X6 (B=Ag+,{~}Na+;
             B{'}=In3+,{~}Bi3+; X=Cl{\ensuremath{-}},{~}Br{\ensuremath{-}}), the
             validity of this common practice is questionable. We find that, for
             these HDPs, the geometrical structures, in particular, the
             B(B{'}){\ensuremath{-}}X bond lengths predicted by PBE show large
             deviations from the experimental values. Additionally, the band gaps
             of some of these materials (specifically, the In-based HDPs) are
             sensitive to the B(B{'}){\ensuremath{-}}X bond lengths. As a
             consequence, the band gaps obtained using the hybrid functionals (such
             as the Heyd-Scuseria-Ernzerhof functional) based on the PBE geometries
             can still be quite off, in particular, for HDPs with B{'}=In3+. The
             situation is significantly improved by using hybrid functionals with
             tuned portion of exact exchange, based on the geometries determined
             consistently under the same level of theory. The successes and
             failures of several popular exchange-correlation (XC) functionals are
             traced back to the so-called delocalization error, and can be
             quantitatively analyzed and understood via a three-atom linear-chain
             B{\ensuremath{-}}X{\ensuremath{-}}B{'} molecular model. Finally, our
             findings provide a practical guide for choosing appropriate XC
             functionals for describing HDPs and point to a promising path for band
             structure engineering via doping and alloying.
             Published by the American Physical Society                 2024},
}
@Article{Chen_MatterRadiatExtrem_2024_v9,
    author =   {Tao Chen and Qianrui Liu and Yu Liu and Liang Sun and Mohan Chen},
    title =    {{Combining stochastic density functional theory with deep potential
             molecular dynamics to study warm dense matter}},
    journal =  {Matter Radiat. Extrem.},
    year =     2024,
    volume =   9,
    number =   1,
    doi =      {10.1063/5.0163303},
    abstract = {In traditional finite-temperature Kohn{\textendash}Sham density
             functional theory (KSDFT), the partial occupation of a large number of
             high-energy KS eigenstates restricts the use of first-principles
             molecular dynamics methods at extremely high temperatures. However,
             stochastic density functional theory (SDFT) can overcome this
             limitation. Recently, SDFT and the related mixed
             stochastic{\textendash}deterministic density functional theory, based
             on a plane-wave basis set, have been implemented in the first-
             principles electronic structure software ABACUS [Q. Liu and M. Chen,
             Phys. Rev. B 106, 125132 (2022)]. In this study, we combine SDFT with
             the Born{\textendash}Oppenheimer molecular dynamics method to
             investigate systems with temperatures ranging from a few tens of eV to
             1000{~}eV. Importantly, we train machine-learning-based interatomic
             models using the SDFT data and employ these deep potential models to
             simulate large-scale systems with long trajectories. Subsequently, we
             compute and analyze the structural properties, dynamic properties, and
             transport coefficients of warm dense matter.},
}
@Article{Liu_JChemTheoryComput_2024_v20_p5717,
    author =   {Jianchuan Liu and Xingchen Zhang and Tao Chen and Yuzhi Zhang and Duo
             Zhang and Linfeng Zhang and Mohan Chen},
    title =    {{Machine-Learning-Based Interatomic Potentials for Group IIB to VIA
             Semiconductors: Toward a Universal Model}},
    journal =  {J. Chem. Theory Comput.},
    year =     2024,
    volume =   20,
    number =   13,
    pages =    {5717--5731},
    doi =      {10.1021/acs.jctc.3c01320},
    abstract = {Rapid advancements in machine-learning methods have led to the
             emergence of machine-learning-based interatomic potentials as a new
             cutting-edge tool for simulating large systems with ab initio
             accuracy. Still, the community awaits universal interatomic models
             that can be applied to a wide range of materials without tuning neural
             network parameters. We develop a unified deep-learning interatomic
             potential (the DPA-Semi model) for 19 semiconductors ranging from
             group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP,
             InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In
             addition, independent deep potential models for each semiconductor are
             prepared for detailed comparison. The training data are obtained by
             performing density functional theory calculations with numerical
             atomic orbitals basis sets to reduce the computational costs. We
             systematically compare various properties of the solid and liquid
             phases of semiconductors between different machine-learning models. We
             conclude that the DPA-Semi model achieves GGA exchange-correlation
             functional quality accuracy and can be regarded as a pretrained model
             toward a universal model to study group IIB to VIA semiconductors.},
}
@Article{Sun_ElectronStruct_2024_v6_p45006,
    author =   {Liang Sun and Mohan Chen},
    title =    {{Multi-channel machine learning based nonlocal kinetic energy density
             functional for semiconductors}},
    journal =  {Electron. Struct.},
    year =     2024,
    volume =   6,
    number =   4,
    pages =    45006,
    doi =      {10.1088/2516-1075/ad8b8c},
    abstract = {Abstract                The recently proposed machine learning-based
             physically-constrained nonlocal (MPN) kinetic energy density
             functional (KEDF) can be used for simple metals and their alloys (Sun
             and Chen 2024 Phys. Rev. B 109 115135). However, the MPN KEDF does not
             perform well for semiconductors. Here we propose a multi-channel MPN
             (CPN) KEDF, which extends the MPN KEDF to semiconductors by
             integrating information collected from multiple channels, with each
             channel featuring a specific length scale in real space. The CPN KEDF
             is systematically tested on silicon and binary semiconductors. We find
             that the multi-channel design for KEDF is beneficial for machine-
             learning-based models in capturing the characteristics of
             semiconductors, particularly in handling covalent bonds. In
             particular, the CPN5 KEDF, which utilizes five channels, demonstrates
             excellent accuracy across all tested systems. These results offer a
             new path for generating KEDFs for semiconductors.},
}
@Article{Liu_PhysRevB_2024_v110_p14207,
    author =   {Qianrui Liu and Mohan Chen},
    title =    {{Effects of nonlocal pseudopotentials on the electrical and thermal
             transport properties of aluminum: A density functional theory study}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   110,
    number =   1,
    pages =    14207,
    doi =      {10.1103/PhysRevB.110.014207},
    abstract = {Accurate prediction of electron transport coefficients is crucial for
             understanding warm dense matter. Utilizing the density functional
             theory (DFT) with the Kubo-Greenwood formula is widely used to
             evaluate the electrical and thermal conductivities of electrons. By
             adding the non-local potential correction term that appears in the
             dynamic Onsager coefficient and using two different norm-conserving
             pseudopotentials, we predict the electrical and thermal conductivities
             of electrons for liquid Al (1000 K) and warm dense Al (0.2 to 10 eV).
             We systematically investigate the effects of non-local terms in the
             pseudopotentials and the frozen-core approximation on the
             conductivities. We find that taking into account the non-local
             potential correction and validating the frozen core approximation is
             essential for accurately calculating the electrical and thermal
             transport properties of electrons across a wide range of temperatures.},
}
@Article{Sun_PhysRevB_2024_v109_p115135,
    author =   {Liang Sun and Mohan Chen},
    title =    {{Machine learning based nonlocal kinetic energy density functional for
             simple metals and alloys}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   11,
    pages =    115135,
    doi =      {10.1103/PhysRevB.109.115135},
    abstract = {Developing an accurate kinetic energy density functional (KEDF)
             remains a major hurdle in orbital-free density functional theory. We
             propose a machine learning based physical-constrained nonlocal (MPN)
             KEDF and implement it with the usage of the bulk-derived local
             pseudopotentials and plane wave basis sets in the ABACUS package. The
             MPN KEDF is designed to satisfy three exact physical constraints: the
             scaling law of electron kinetic energy, the free electron gas limit,
             and the non-negativity of Pauli energy density. The MPN KEDF is
             systematically tested for simple metals, including Li, Mg, Al, and 59
             alloys. We conclude that incorporating nonlocal information for
             designing new KEDFs and obeying exact physical constraints are
             essential to improve the accuracy, transferability, and stability of
             ML-based KEDF. These results shed new light on the construction of ML-
             based functionals.},
}
@Article{Chen_MatterRadiatExtrem_2024_v9,
    author =   {Tao Chen and Qianrui Liu and Yu Liu and Liang Sun and Mohan Chen},
    title =    {{Combining stochastic density functional theory with deep potential
             molecular dynamics to study warm dense matter}},
    journal =  {Matter Radiat. Extrem.},
    year =     2024,
    volume =   9,
    number =   1,
    doi =      {10.1063/5.0163303},
    abstract = {In traditional finite-temperature Kohn{\textendash}Sham density
             functional theory (KSDFT), the partial occupation of a large number of
             high-energy KS eigenstates restricts the use of first-principles
             molecular dynamics methods at extremely high temperatures. However,
             stochastic density functional theory (SDFT) can overcome this
             limitation. Recently, SDFT and the related mixed
             stochastic{\textendash}deterministic density functional theory, based
             on a plane-wave basis set, have been implemented in the first-
             principles electronic structure software ABACUS [Q. Liu and M. Chen,
             Phys. Rev. B 106, 125132 (2022)]. In this study, we combine SDFT with
             the Born{\textendash}Oppenheimer molecular dynamics method to
             investigate systems with temperatures ranging from a few tens of eV to
             1000{~}eV. Importantly, we train machine-learning-based interatomic
             models using the SDFT data and employ these deep potential models to
             simulate large-scale systems with long trajectories. Subsequently, we
             compute and analyze the structural properties, dynamic properties, and
             transport coefficients of warm dense matter.},
}
@Article{Wu_PhysRevB_2023_v108_pL180104,
    author =   {Jing Wu and Jiyuan Yang and Yuan-Jinsheng Liu and Duo Zhang and Yudi
             Yang and Yuzhi Zhang and Linfeng Zhang and Shi Liu},
    title =    {{Universal interatomic potential for perovskite oxides}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   108,
    number =   18,
    pages =    {L180104},
    doi =      {10.1103/PhysRevB.108.L180104},
    abstract = {With their celebrated structural and chemical flexibility, perovskite
             oxides have served as a highly adaptable material platform for
             exploring emergent phenomena arising from the interplay between
             different degrees of freedom. Molecular dynamics (MD) simulations
             leveraging classical force fields, commonly depicted as parameterized
             analytical functions, have made significant contributions in
             elucidating the atomistic dynamics and structural properties of
             crystalline solids including perovskite oxides. However, the force
             fields currently available for solids are rather specific and offer
             limited transferability, making it time-consuming to use MD to study
             new materials systems since a new force field must be parameterized
             and tested first. The lack of a generalized force field applicable to
             a broad spectrum of solid materials hinders the facile deployment of
             MD in computer-aided materials discovery (CAMD). Here, by utilizing a
             deep-neural network with a self-attention scheme, we have developed a
             unified force field that enables MD simulations of perovskite oxides
             involving 14 metal elements and conceivably their solid solutions with
             arbitrary compositions. Notably, isobaric-isothermal ensemble MD
             simulations with this model potential accurately predict the
             experimental phase transition sequences for several markedly different
             ferroelectric oxides, including a 6-element ternary solid solution, Pb
             (In{\$}{\_}{\{}1/2{\}}{\$}Nb{\$}{\_}{\{}1/2{\}}{\$})O{\$}{\_}3{\$}--
             Pb(Mg{\$}{\_}{\{}1/3{\}}{\$}Nb{\$}{\_}{\{}2/3{\}}{\$})O{\$}{\_}3{\$}--
             PbTiO{\$}{\_}3{\$}. We believe the universal interatomic potential
             along with the training database, proposed regression tests, and the
             auto-testing workflow, all released publicly, will pave the way for a
             systematic improvement and extension of a unified force field for
             solids, potentially heralding a new era in CAMD.},
}
@Article{Gillen_JPhysCondensMatterInstPhysJ_2024_v36_p295501,
    author =   {Roland Gillen and Janina Maultzsch},
    title =    {{Family behavior and Dirac bands in armchair nanoribbons with 4-8
             defect lines}},
    journal =  {J. Phys., Condens. Matter: Inst. Phys. J.},
    year =     2024,
    volume =   36,
    number =   29,
    pages =    295501,
    doi =      {10.1088/1361-648X/ad3b5a},
    abstract = {Bottom-up synthesis from molecular precursors is a powerful route for
             the creation of novel synthetic carbon-based low-dimensional
             materials, such as planar carbon lattices. The wealth of conceivable
             precursor molecules introduces a significant number of degrees-of-
             freedom for the design of materials with defined physical properties.
             In this context,a prioriknowledge of the electronic, vibrational and
             optical properties provided by modernab initiosimulation methods can
             act as a valuable guide for the design of novel synthetic carbon-based
             building blocks. Using density functional theory, we performed
             simulations of the electronic properties of armchair-edged graphene
             nanoribbons (AGNR) with a bisecting 4-8 ring defect line. We show that
             the electronic structures of the defective nanoribbons of increasing
             width can be classified into three distinct families of
             semiconductors, similar to the case of pristine AGNR. In contrast to
             the latter, we find that every third nanoribbon is a zero-gap
             semiconductor with Dirac-type crossing of linear bands at the Fermi
             energy. By employing tight-binding models including interactions up to
             third-nearest neighbors, we show that the family behavior, the
             formation of direct and indirect band gaps and of linear band
             crossings in the defective nanoribbons is rooted in the electronic
             properties of the individual nanoribbon halves on either side of the
             defect lines, and can be effectively through introduction of
             additional 'interhalf' coupling terms.},
}
@Article{Chen_AcsNano_2024_v18_p13035,
    author =   {Jinghang Chen and Yuqi Ren and Yiwei Fu and Yitao Si and Jie Huang and
             Jiancheng Zhou and Maochang Liu and Lunbo Duan and Naixu Li},
    title =    {{Integration of Co Single Atoms and Ni Clusters on Defect-Rich ZrO2 for
             Strong Photothermal Coupling Boosts Photocatalytic CO2 Reduction}},
    journal =  {Acs Nano},
    year =     2024,
    volume =   18,
    number =   20,
    pages =    {13035--13048},
    doi =      {10.1021/acsnano.4c01637},
    abstract = {We report a solvothermal method for the synthesis of an oxygen
             vacancy-enriched ZrO2 photocatalyst with Co single atoms and Ni
             clusters immobilized on the surface. This catalyst presents superior
             performance for the reduction of CO2 in H2O vapor, with a CO yield
             reaching 663.84 {\ensuremath{\mu}}mol g-1 h-1 and a selectivity of
             99.52{\%}. The total solar-to-chemical energy conversion efficiency is
             up to 0.372{\textperthousand}, which is among the highest reported
             values. The success, on one hand, depends on the Co single atoms and
             Ni clusters for both extended spectrum absorption and serving as dual-
             active centers for CO2 reduction and H2O dissociation, respectively;
             on the other hand, this is attributed to the enhanced photoelectric
             and thermal effect induced by concentrated solar irradiation. We
             demonstrate that an intermediate impurity state is formed by the
             hybridization of the d-orbital of single-atom Co with the molecular
             orbital of H2O, enabling visible-light-driven excitation over the
             catalyst. In addition, Ni clusters play a crucial role in altering the
             adsorption configuration of CO2, with the localized surface plasmon
             resonance effect enhancing the activation and dissociation of CO2
             induced by visible-near-infrared light. This study provides valuable
             insights into the synergistic effect of the dual cocatalyst toward
             both efficient photothermal coupling and surface redox reactions for
             solar CO2 reduction.},
}
@Article{Ren_NatCommun_2024_v15_p4675,
    author =   {Yuqi Ren and Yiwei Fu and Naixu Li and Changjun You and Jie Huang and
             Kai Huang and Zhenkun Sun and Jiancheng Zhou and Yitao Si and Yuanhao
             Zhu and Wenshuai Chen and Lunbo Duan and Maochang Liu},
    title =    {{Concentrated solar CO2 reduction in H2O vapour with
             {\ensuremath{>}}1{\%} energy conversion efficiency}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    4675,
    doi =      {10.1038/s41467-024-49003-8},
    abstract = {H2O dissociation plays a crucial role in solar-driven catalytic CO2
             methanation, demanding high temperature even for solar-to-chemical
             conversion efficiencies {\ensuremath{<}}1{\%} with modest product
             selectivity. Herein, we report an oxygen-vacancy (Vo) rich CeO2
             catalyst with single-atom Ni anchored around its surface Vo sites by
             replacing Ce atoms to promote H2O dissociation and achieve effective
             photothermal CO2 reduction under concentrated light irradiation. The
             high photon flux reduces the apparent activation energy for CH4
             production and prevents Vo from depletion. The defects coordinated
             with single-atom Ni, significantly promote the capture of charges and
             local phonons at the Ni d-impurity orbitals, thereby inducing more
             effective H2O activation. The catalyst presents a CH4 yield of 192.75
             {\textmu}mol/cm2/h, with a solar-to-chemical efficiency of 1.14{\%}
             and a selectivity {\textasciitilde}100{\%}. The mechanistic insights
             uncovered in this study should help further the development of
             H2O-activating catalysts for CO2 reduction and thereby expedite the
             practical utilization of solar-to-chemical technologies.},
}
@Article{Zhang_JChemInfModel_2024_v64_p3599,
    author =   {Pengchao Zhang and Axel Tosello Gardini and Xuefei Xu and Michele
             Parrinello},
    title =    {{Intramolecular and Water Mediated Tautomerism of Solvated Glycine}},
    journal =  {J. Chem. Inf. Model.},
    year =     2024,
    volume =   64,
    number =   9,
    pages =    {3599--3604},
    doi =      {10.1021/acs.jcim.4c00273},
    abstract = {Understanding tautomerism and characterizing solvent effects on the
             dynamic processes pose significant challenges. Using enhanced-sampling
             molecular dynamics based on state-of-the-art deep learning potentials,
             we investigated the tautomeric equilibria of glycine in water. We
             observed that the tautomerism between neutral and zwitterionic glycine
             can occur through both intramolecular and intermolecular proton
             transfers. The latter proceeds involving a contact anionic-glycine-
             hydronium ion pair or separate cationic-glycine-hydroxide ion pair.
             These pathways with comparable barriers contribute almost equally to
             the reaction flux.},
}
@Article{Li_PhysRevB_2024_v109_p85118,
    author =   {Yonggang Li and Zhen Zhan and Shengjun Yuan},
    title =    {{Tuning flat bands by interlayer interaction, spin-orbital coupling,
             and external fields in twisted homotrilayer  MoS2}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   8,
    pages =    85118,
    doi =      {10.1103/PhysRevB.109.085118},
    abstract = {Ultraflat bands have already been detected in twisted bilayer graphene
             and twisted bilayer transition-metal dichalcogenides, which provide a
             platform to investigate strong correlations. In this paper, the
             electronic properties of twisted trilayer molybdenum disulfide (TTM)
             are investigated via an accurate tight-binding Hamiltonian. We find
             that the highest valence bands are derived from the
             {\$}{\textbackslash}Gamma{\$}-point of the constituent monolayer, and
             they exhibit a graphenelike dispersion or become isolated flat bands
             that are dependent on the starting stacking arrangements. The lattice
             relaxation, local deformation, and external fields can significantly
             tune the electronic structures of TTM. After introducing the spin-
             orbital coupling effect, we find a spin-valley-layer locking effect at
             the minimum of the conduction band at the {\$}K{\$}- and
             {\$}K{\textasciicircum}{\textbackslash}prime{\$}-point of the
             Brillouin zone, which may provide a platform to study optical
             properties and magnetoelectric effects.},
}
@Article{Zhong_NatComputSci_2024_v4_p615,
    author =   {Yang Zhong and Shixu Liu and Binhua Zhang and Zhiguo Tao and Yuting
             Sun and Weibin Chu and Xin-Gao Gong and Ji-Hui Yang and Hongjun Xiang},
    title =    {{Accelerating the calculation of electron-phonon coupling strength with
             machine learning}},
    journal =  {Nat. Comput. Sci.},
    year =     2024,
    volume =   4,
    number =   8,
    pages =    {615--625},
    doi =      {10.1038/s43588-024-00668-7},
    abstract = {The calculation of electron-phonon couplings (EPCs) is essential for
             understanding various fundamental physical properties, including
             electrical transport, optical and superconducting behaviors in
             materials. However, obtaining EPCs through fully first-principles
             methods is notably challenging, particularly for large systems or when
             employing advanced functionals. Here we introduce a machine learning
             framework to accelerate EPC calculations by utilizing atomic orbital-
             based Hamiltonian matrices and gradients predicted by an equivariant
             graph neural network. We demonstrate that our method not only yields
             EPC values in close agreement with first-principles results but also
             enhances calculation efficiency by several orders of magnitude.
             Application to GaAs using the Heyd-Scuseria-Ernzerhof functional
             reveals the necessity of advanced functionals for accurate carrier
             mobility predictions, while for the large Kagome crystal CsV3Sb5, our
             framework reproduces the experimentally observed double domes in
             pressure-induced superconducting phase diagrams. This machine learning
             framework offers a powerful and efficient tool for the investigation
             of diverse EPC-related phenomena in complex materials.},
}
@Article{Li_JPhysChemA_2022_v126_p9154,
    author =   {Wenfei Li and Qi Ou and Yixiao Chen and Yu Cao and Renxi Liu and
             Chunyi Zhang and Daye Zheng and Chun Cai and Xifan Wu and Han Wang and
             Mohan Chen and Linfeng Zhang},
    title =    {{DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical
             Models and Machine Learning Potentials}},
    journal =  {J. Phys. Chem., A},
    year =     2022,
    volume =   126,
    number =   49,
    pages =    {9154--9164},
    doi =      {10.1021/acs.jpca.2c05000},
    abstract = {Recently, the development of machine learning (ML) potentials has made
             it possible to perform large-scale and long-time molecular simulations
             with the accuracy of quantum mechanical (QM) models. However, for
             different levels of QM methods, such as density functional theory
             (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte
             Carlo, etc., generating a sufficient amount of data for training an ML
             potential has remained computationally challenging due to their high
             cost. In this work, we demonstrate that this issue can be largely
             alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS
             employs a computationally efficient neural network-based functional
             model to construct a correction term added upon a cheap DFT model.
             Upon training, DeePKS offers closely matched energies and forces
             compared with high-level QM method, but the number of training data
             required is orders of magnitude less than that required for training a
             reliable ML potential. As such, DeePKS can serve as a bridge between
             expensive QM models and ML potentials: one can generate a decent
             amount of high-accuracy QM data to train a DeePKS model and then use
             the DeePKS model to label a much larger amount of configurations to
             train an ML potential. This scheme for periodic systems is implemented
             in a DFT package ABACUS, which is open source and ready for use in
             various applications.},
}
@Article{Ou_JPhysChemC_2023_v127_p18755,
    author =   {Qi Ou and Ping Tuo and Wenfei Li and Xiaoxu Wang and Yixiao Chen and
             Linfeng Zhang},
    title =    {{DeePKS Model for Halide Perovskites with the Accuracy of a Hybrid
             Functional}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    number =   37,
    pages =    {18755--18764},
    doi =      {10.1021/acs.jpcc.3c04703},
    abstract = {Accurate prediction for the electronic structure properties of halide
             perovskites plays a significant role in the design of highly efficient
             and stable solar cells. While density functional theory (DFT) within
             the generalized gradient approximation (GGA) offers reliable
             prediction in terms of lattice constants and potential energy surface
             for halide perovskites, it severely underestimates the band gap due to
             the lack of non-local exact exchange term, which exists in
             computationally expensive hybrid functionals. In this work, a
             universal Deep Kohn-Sham (DeePKS) model based on neural network is
             trained so as to enable electronic structure calculations with the
             accuracy of hybrid functional HSE06 and the efficiency comparable to
             GGA functional, for a plethora of halide perovskites, i.e.,
             ABX{\$}{\_}3{\$} (A=FA, MA, Cs; B=Sn, Pb; X=Cl, Br, I). Forces, band
             gaps, and density of states (DOS) predicted by our DeePKS model for
             all aforementioned perovskites are in good agreement with the HSE06
             results, with significantly improved efficiency. In addition, even
             though the spin-orbit coupling (SOC) effect has not been taken into
             consideration during the training process, DeePKS+SOC offers highly
             consistent band gap and DOS as compared to HSE06+SOC for Pb-containing
             systems. We believe such DeePKS model can be readily applied for an
             accurate yet efficient prediction of various properties for the family
             of halide perovskites.},
}

@Article{Li_ComputMaterSci_2016_v112_p503,
    author =   {Pengfei Li and Xiaohui Liu and Mohan Chen and Peize Lin and Xinguo Ren
             and Lin Lin and Chao Yang and Lixin He},
    title =    {{Large-scale ab initio simulations based on systematically improvable
             atomic basis}},
    journal =  {Comput. Mater. Sci.},
    year =     2016,
    volume =   112,
    pages =    {503--517},
    doi =      {10.1016/j.commatsci.2015.07.004},
}
@Article{Liu_PhysRevB_2022_v106_p125132,
    author =   {Qianrui Liu and Mohan Chen},
    title =    {{Plane-wave-based stochastic-deterministic density functional theory
             for extended systems}},
    journal =  {Phys. Rev. B},
    year =     2022,
    volume =   106,
    number =   12,
    pages =    125132,
    doi =      {10.1103/PhysRevB.106.125132},
    abstract = {Traditional finite-temperature Kohn-Sham density functional theory
             (KSDFT) has an unfavorable scaling with respect to the electron number
             or at high temperatures. The evaluation of the ground-state density in
             KSDFT can be replaced by the Chebyshev trace (CT) method. In addition,
             the use of stochastic orbitals within the CT method leads to the
             stochastic density functional theory [Phys. Rev. Lett. 111, 106402
             (2013)] (SDFT) and its improved theory, mixed stochastic-deterministic
             density functional theory [Phys. Rev. Lett. 125, 055002 (2020)]
             (MDFT). We have implemented the above four methods within the first-
             principles package ABACUS. All of the four methods are based on the
             plane-wave basis set with the use of norm-conserving pseudopotentials
             and the periodic boundary conditions with the use of {\$}k{\$}-point
             sampling in the Brillouin zone. By using the KSDFT calculation results
             as benchmarks, we systematically evaluate the accuracy and efficiency
             of the CT, SDFT, and MDFT methods via examining a series of physical
             properties, which include the electron density, the free energy, the
             atomic forces, stress, and density of states for a few condensed phase
             systems. The results suggest that our implementations of CT, SDFT, and
             MDFT not only reproduce the KSDFT results with a high accuracy, but
             also exhibit several advantages over the tradition KSDFT method. We
             expect these methods can be of great help in studying high-temperature
             and large-size extended systems such as warm dense matter and dense
             plasma.},
}
@Article{Chen_JPhysCondensMatterInstPhysJ_2010_v22_p445501,
    author =   {Mohan Chen and G-C Guo and Lixin He},
    title =    {{Systematically improvable optimized atomic basis sets for ab initio
             calculations}},
    journal =  {J. Phys., Condens. Matter: Inst. Phys. J.},
    year =     2010,
    volume =   22,
    number =   44,
    pages =    445501,
    doi =      {10.1088/0953-8984/22/44/445501},
    abstract = {We propose a unique scheme to construct fully optimized atomic basis
             sets for density-functional calculations. The shapes of the radial
             functions are optimized by minimizing the spillage of the
             wavefunctions between the atomic orbital calculations and the
             converged plane wave results for dimer systems. The quality of the
             bases can be systematically improved by increasing the size of the
             bases within the same framework. We show that the spillage can
             describe the convergency of the total energy very well and the cutoff
             radii of the atomic orbitals are extremely important for the quality
             of the atomic orbitals. The scheme is easy to implement and very
             flexible. We have performed extensive tests of this scheme for a wide
             variety of systems, including semiconductors, oxides, metals,
             clusters, etc. The results show that the obtained atomic bases are
             very satisfactory for both accuracy and transferability.},
}
@Article{Chen_JPhysCondensMatterInstPhysJ_2011_v23_p325501,
    author =   {Mohan Chen and G-C Guo and Lixin He},
    title =    {{Electronic structure interpolation via atomic orbitals}},
    journal =  {J. Phys., Condens. Matter: Inst. Phys. J.},
    year =     2011,
    volume =   23,
    number =   32,
    pages =    325501,
    doi =      {10.1088/0953-8984/23/32/325501},
    abstract = {We present an efficient scheme for accurate electronic structure
             interpolation based on systematically improvable optimized atomic
             orbitals. The atomic orbitals are generated by minimizing the spillage
             value between the atomic basis calculations and the converged plane
             wave basis calculations on some coarse k-point grid. They are then
             used to calculate the band structure of the full Brillouin zone using
             the linear combination of atomic orbitals algorithms. We find that
             usually 16-25 orbitals per atom can give an accuracy of about 10 meV
             compared to the full ab initio calculations, and the accuracy can be
             systematically improved by using more atomic orbitals. The scheme is
             easy to implement and robust, and works equally well for metallic
             systems and systems with complicated band structures. Furthermore, the
             atomic orbitals have much better transferability than Shirley's basis
             and Wannier functions, which is very useful for perturbation
             calculations.},
}
@Article{Lin_JPhysCondensMatterInstPhysJ_2013_v25_p295501,
    author =   {Lin Lin and Mohan Chen and Chao Yang and Lixin He},
    title =    {{Accelerating atomic orbital-based electronic structure calculation via
             pole expansion and selected inversion}},
    journal =  {J. Phys., Condens. Matter: Inst. Phys. J.},
    year =     2013,
    volume =   25,
    number =   29,
    pages =    295501,
    doi =      {10.1088/0953-8984/25/29/295501},
    abstract = {We describe how to apply the recently developed pole expansion and
             selected inversion (PEXSI) technique to Kohn-Sham density function
             theory (DFT) electronic structure calculations that are based on
             atomic orbital discretization. We give analytic expressions for
             evaluating the charge density, the total energy, the Helmholtz free
             energy and the atomic forces (including both the Hellmann-Feynman
             force and the Pulay force) without using the eigenvalues and
             eigenvectors of the Kohn-Sham Hamiltonian. We also show how to update
             the chemical potential without using Kohn-Sham eigenvalues. The
             advantage of using PEXSI is that it has a computational complexity
             much lower than that associated with the matrix diagonalization
             procedure. We demonstrate the performance gain by comparing the timing
             of PEXSI with that of diagonalization on insulating and metallic
             nanotubes. For these quasi-1D systems, the complexity of PEXSI is
             linear with respect to the number of atoms. This linear scaling can be
             observed in our computational experiments when the number of atoms in
             a nanotube is larger than a few hundreds. Both the wall clock time and
             the memory requirement of PEXSI are modest. This even makes it
             possible to perform Kohn-Sham DFT calculations for 10{\,}000-atom
             nanotubes with a sequential implementation of the selected inversion
             algorithm. We also perform an accurate geometry optimization
             calculation on a truncated (8, 0) boron nitride nanotube system
             containing 1024 atoms. Numerical results indicate that the use of
             PEXSI does not lead to loss of the accuracy required in a practical
             DFT calculation.},
}
@Article{Liu_JChemPhys_2017_v147_p64505,
    author =   {Xiaohui Liu and Daye Zheng and Xinguo Ren and Lixin He and Mohan Chen},
    title =    {{First-principles molecular dynamics study of deuterium diffusion in
             liquid tin}},
    journal =  {J. Chem. Phys.},
    year =     2017,
    volume =   147,
    number =   6,
    pages =    64505,
    doi =      {10.1063/1.4997635},
    abstract = {Understanding the retention of hydrogen isotopes in liquid metals,
             such as lithium and tin, is of great importance in designing a liquid
             plasma-facing component in fusion reactors. However, experimental
             diffusivity data of hydrogen isotopes in liquid metals are still
             limited or controversial. We employ first-principles molecular
             dynamics simulations to predict diffusion coefficients of deuterium in
             liquid tin at temperatures ranging from 573 to 1673 K. Our simulations
             indicate faster diffusion of deuterium in liquid tin than the self-
             diffusivity of tin. In addition, we find that the structural and
             dynamic properties of tin are insensitive to the inserted deuterium at
             temperatures and concentrations considered. We also observe that tin
             and deuterium do not form stable solid compounds. These predicted
             results from simulations enable us to have a better understanding of
             the retention of hydrogen isotopes in liquid tin.},
}
@Article{Li_PhysRevB_2017_v96_p165417,
    author =   {Pengfei Li and Xinguo Ren and Lixin He},
    title =    {{First-principles calculations and model analysis of plasmon
             excitations in graphene and graphene/hBN heterostructure}},
    journal =  {Phys. Rev. B},
    year =     2017,
    volume =   96,
    number =   16,
    pages =    165417,
    doi =      {10.1103/PhysRevB.96.165417},
    abstract = {Plasmon excitations in free-standing graphene and graphene/hexagonal
             boron nitride (hBN) heterostructure are studied using linear-response
             time-dependent density functional theory within the random phase
             approximation. Within a single theoretical framework, we examine both
             the plasmon dispersion behavior and lifetime (line width) of Dirac and
             {\$}{\textbackslash}pi{\$} plasmons on an equal footing. Particular
             attention is paid to the influence of the hBN substrate and the
             anisotropic effect. Furthermore, a model-based analysis indicates that
             the correct dispersion behavior of {\$}{\textbackslash}pi{\$} plasmons
             should be {\$}{\textbackslash}omega{\_}{\textbackslash}pi(q) =
             {\textbackslash}sqrt{\{}E{\_}g{\textasciicircum}2 +
             {\textbackslash}beta q{\}}{\$} for small {\$}q{\$}'s, where
             {\$}E{\_}g{\$} is the band gap at the {\$}M{\$} point in the Brillouin
             zone, and {\$}{\textbackslash}beta{\$} is a fitting parameter. This
             model is radically different from previous proposals, but in good
             agreement with our calculated results from first principles.},
}
@Article{Liu_GeochimCosmochimActa_2018_v223_p364,
    author =   {Xiaohui Liu and Yuhan Qi and Daye Zheng and Chen Zhou and Lixin He and
             Fang Huang},
    title =    {{Diffusion coefficients of Mg isotopes in MgSiO3 and Mg2SiO4 melts
             calculated by first-principles molecular dynamics simulations}},
    journal =  {Geochim. Cosmochim. Acta},
    year =     2018,
    volume =   223,
    pages =    {364--376},
    doi =      {10.1016/j.gca.2017.12.007},
}
@Article{Wang_PhysRevAppl_2019_v12_p44060,
    author =   {Qianpeng Wang and Daye Zheng and Lixin He and Xinguo Ren},
    title =    {{Cooperative Effect in a Graphite Intercalation Compound: Enhanced
             Mobility of  AlCl4  in the Graphite Cathode of Aluminum-Ion Batteries}},
    journal =  {Phys, Rev, Appl.},
    year =     2019,
    volume =   12,
    number =   4,
    pages =    44060,
    doi =      {10.1103/PhysRevApplied.12.044060},
    abstract = {Graphite is a common electrode material in metal-ion batteries, and
             understanding the diffusion of atoms and small molecules in it is
             instrumental for improving graphite-based batteries. Here first-
             principles molecular dynamics simulations reveal an intriguing
             cooperative effect in AlCl{\$}{\{}{\}}{\_}{\{}4{\}}{\$}-intercalated
             graphite, whereby the motions of the graphene layers and the
             intercalant AlCl{\$}{\{}{\}}{\_}{\{}4{\}}{\$} molecules considerably
             enhance each other. This cooperative behavior is a key factor behind
             the ultrahigh-rate capability of a recently reported aluminum-ion
             battery. Furthermore, it turns out that an
             AlF{\$}{\{}{\}}{\_}{\{}4{\}}{\$}-based battery could be even better.},
}
@Article{Liu_JChemPhys_2019_v151_p215102,
    author =   {Yue Liu and Xinguo Ren and Lixin He},
    title =    {{A DFT study of energetic and structural properties of a full turn of
             A-form DNA under relaxed and stretching conditions}},
    journal =  {J. Chem. Phys.},
    year =     2019,
    volume =   151,
    number =   21,
    pages =    215102,
    doi =      {10.1063/1.5129716},
    abstract = {We carried out a first-principles quantum-mechanical study of a set of
             full-turn A-form DNA oligonucleotides using density functional
             approximations. The structural features of the fully relaxed model DNA
             molecules and more importantly, the energetic and structural changes
             of these molecules under stretching conditions are examined in detail.
             Special attention is paid to the dependence of the structural
             properties on the stacking sequences of the constituent DNA base
             pairs. It appears that DNA oligonucleotides are extremely flexible,
             and structural properties such as the diameter and the widths of the
             minor and major grooves change regularly as a function of the
             stretching rate (stretching length in percentage). Additionally, we
             found that the length of the hydrogen bonds in the DNA base pairs
             varies with the DNA stacking sequence. This work indicates that rich
             information and insights can be gained from first principles studies
             of model DNA molecules of only O(102-103) atoms.},
}
@Article{Shao_RscAdv_2019_v9_p2870,
    author =   {Xuecheng Shao and Xin Qu and Siyu Liu and Lihua Yang and Jinghai Yang
             and Xiaohui Liu and Xin Zhong and Shuai Sun and G. Vaitheeswaran and
             Jian Lv},
    title =    {{Structure evolution of chromium-doped boron clusters: toward the
             formation of endohedral boron cages}},
    journal =  {Rsc Adv.},
    year =     2019,
    volume =   9,
    number =   5,
    pages =    {2870--2876},
    doi =      {10.1039/C8RA09143A},
    abstract = {The effect of chromium doping on the structure evolution of small-
             sized boron clusters.},
}
@Article{Zheng_SciAdv_2020_v6_peaba0826,
    author =   {Yu Zheng and Lei Liu and Hanqing Nan and Zhen-Xiong Shen and Ge Zhang
             and Duyu Chen and Lixin He and Wenxiang Xu and Mohan Chen and Yang
             Jiao and Houlong Zhuang},
    title =    {{Disordered hyperuniformity in two-dimensional amorphous silica}},
    journal =  {Sci. Adv.},
    year =     2020,
    volume =   6,
    number =   16,
    pages =    {eaba0826},
    doi =      {10.1126/sciadv.aba0826},
    abstract = {Disordered hyperuniformity (DHU) is a recently proposed new state of
             matter, which has been observed in a variety of classical and quantum
             many-body systems. DHU systems are characterized by vanishing
             infinite-wavelength normalized density fluctuations and are endowed
             with unique novel physical properties. Here, we report the discovery
             of disordered hyperuniformity in atomic-scale two-dimensional
             materials, i.e., amorphous silica composed of a single layer of atoms,
             based on spectral-density analysis of high-resolution transmission
             electron microscopy images. Moreover, we show via large-scale density
             functional theory calculations that DHU leads to almost complete
             closure of the electronic bandgap compared to the crystalline
             counterpart, making the material effectively a metal. This is in
             contrast to the conventional wisdom that disorder generally diminishes
             electronic transport and is due to the unique electron wave
             localization induced by the topological defects in the DHU state.},
}
@Article{Yang_NanoLett_2020_v20_p758,
    author =   {Hai Yang and Lin-Wei Chen and Fuxiang He and Jiaqing Zhang and Yuezhan
             Feng and Lukang Zhao and Bin Wang and Lixin He and Qiaobao Zhang and
             Yan Yu},
    title =    {{Optimizing the Void Size of Yolk-Shell Bi@Void@C Nanospheres for High-
             Power-Density Sodium-Ion Batteries}},
    journal =  {Nano Lett.},
    year =     2020,
    volume =   20,
    number =   1,
    pages =    {758--767},
    doi =      {10.1021/acs.nanolett.9b04829},
    abstract = {Bismuth (Bi) has been demonstrated as a promising anode for Na-ion
             batteries (NIBs) because it has high gravimetry (386 mA h g-1) and
             volumetric capacity (3800 mA h cm-3). However, Bi suffers from large
             volume expansion during sodiation, leading to poor electrochemical
             performance. The construction of a nanostructure with sufficient void
             space to accommodate the volume change has been proven effective for
             achieving prolonged cycling stability. However the excessive void
             space will definitely decrease the volumetric energy density of the
             battery. Herein, we design optimized Bi@Void@C nanospheres
             (Bi@Void@C-2) with yolk-shell structure that exhibit the best cycling
             performance and enhanced volumetric energy density. The optimized void
             space not only could buffer the volume change of the Bi nanosphere but
             also could keep the high volumetric energy density of the battery. The
             Bi@Void@C-2 shows an excellent rate capacity of 173 mA h g-1 at
             ultrahigh current density of 100 A g-1 and long-cycle life (198 mA h
             g-1 at 20 A g-1 over 10 000 cycles). The origin of the superior
             performance is achieved through in-depth fundamental studies during
             battery operation using in situ X-ray diffraction (XRD) and in situ
             transmission electron microscope (TEM), complemented by theoretical
             calculations and ex situ TEM observation. Our rational design provides
             insights for anode materials with large volume variation, especially
             for conversion type and alloying type mechanism materials for
             batteries (i.e., Li-ion batteries, Na-ion batteries).},
}
@Article{Wang_PhysRevB_2020_v101_p165137,
    author =   {Lian-Wei Wang and Li-Shan Xie and Peng-Xiang Xu and Ke Xia},
    title =    {{First-principles study of magnon-phonon interactions in gadolinium
             iron garnet}},
    journal =  {Phys. Rev. B},
    year =     2020,
    volume =   101,
    number =   16,
    pages =    165137,
    doi =      {10.1103/PhysRevB.101.165137},
    abstract = {We obtained the spin-wave spectrum based on a first-principles method
             of exchange constants, calculated the phonon spectrum by the first-
             principles phonon calculation method, and extracted the broadening of
             the magnon spectrum {\$}{\textbackslash}mathrm{\{}{\textbackslash}ensu
             remath{\{}{\textbackslash}Delta{\}}{\}}{\textbackslash}ensuremath{\{}{
             \textbackslash}omega{\}}{\$}, induced by magnon-phonon interactions in
             gadolinium iron garnet (GdIG). Using the obtained exchange constants,
             we reproduce the experimental Curie temperature and the compensation
             temperature from spin models using metropolis Monte Carlo (MC)
             simulations. In the lower-frequency regime, the fitted positions of
             the magnon-phonon dispersion crossing points are consistent with the
             inelastic neutron scattering experiment. We found that the {\$}{\textb
             ackslash}mathrm{\{}{\textbackslash}ensuremath{\{}{\textbackslash}Delta
             {\}}{\}}{\textbackslash}ensuremath{\{}{\textbackslash}omega{\}}{\$}
             and magnon wave vector {\$}k{\$} have a similar relationship in YIG.
             The broadening of the acoustic spin-wave branch
             ({\$}{\textbackslash}ensuremath{\{}{\textbackslash}alpha{\}}{\$} mode)
             is proportional to {\$}{\{}k{\}}{\textasciicircum}{\{}2{\}}{\$}, while
             that of the optical branch
             ({\$}{\textbackslash}ensuremath{\{}{\textbackslash}beta{\}}{\$} and
             {\$}{\textbackslash}ensuremath{\{}{\textbackslash}gamma{\}}{\$} modes)
             is a constant. At a specific {\$}k{\$}, the magnon-phonon
             thermalization times of {\$}{\{}{\textbackslash}ensuremath{\{}{\textba
             ckslash}tau{\}}{\}}{\_}{\{}{\textbackslash}mathrm{\{}mp{\}}{\}}{\$}
             are approximately {\$}{\{}10{\}}{\textasciicircum}{\{}{\textbackslash}
             ensuremath{\{}-{\}}9{\}}{\$}, {\$}{\{}10{\}}{\textasciicircum}{\{}{\te
             xtbackslash}ensuremath{\{}-{\}}13{\}}{\$}, and {\$}{\{}10{\}}{\textasc
             iicircum}{\{}{\textbackslash}ensuremath{\{}-
             {\}}14{\}}{\textbackslash}phantom{\{}{\textbackslash}rule{\{}0.16em{\}
             }{\{}0ex{\}}{\}}{\textbackslash}mathrm{\{}s{\}}{\$} for
             {\$}{\textbackslash}ensuremath{\{}{\textbackslash}alpha{\}}{\$},
             {\$}{\textbackslash}ensuremath{\{}{\textbackslash}beta{\}}{\$}, and
             {\$}{\textbackslash}ensuremath{\{}{\textbackslash}gamma{\}}{\$} modes,
             respectively. The results show the importance of the higher frequency
             modes at room temperature. This research provides specific and
             effective information for developing a clear understanding of the
             spin-wave mediated spin Seebeck effect and complements the lack of
             lattice dynamics calculations of GdIG.},
}
@Article{Lin_JPhysChemLett_2020_v11_p3082,
    author =   {Peize Lin and Xinguo Ren and Lixin He},
    title =    {{Accuracy of Localized Resolution of the Identity in Periodic Hybrid
             Functional Calculations with Numerical Atomic Orbitals}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2020,
    volume =   11,
    number =   8,
    pages =    {3082--3088},
    doi =      {10.1021/acs.jpclett.0c00481},
    abstract = {We present an implementation of hybrid density functional
             approximations for periodic systems within a pseudopotential-based,
             numerical atomic orbital (NAO) framework. The two-electron Coulomb
             repulsion integrals (ERIs) are evaluated using the localized
             resolution-of-the-identity (LRI) approximation. The accuracy of the
             LRI approximation is benchmarked unambiguously against independent
             reference results obtained via a computational scheme whereby the ERIs
             are accurately evaluated by expanding the products of NAOs in terms of
             plane waves. An alternative strategy for constructing auxiliary basis
             sets is proposed, and its accuracy is assessed and compared to the
             previously used procedure. Finally, the reliability of our algorithm
             and implementation is benchmarked against other established
             implementations within different numerical frameworks in terms of the
             calculated band gap values of a set of semiconductors and insulators.},
}
@Article{Zheng_JNuclMater_2021_v543_p152542,
    author =   {Daye Zheng and Zhen-Xiong Shen and Mohan Chen and Xinguo Ren and Lixin
             He},
    title =    {{Retention and recycling of deuterium in liquid lithium-tin slab
             studied by first-principles molecular dynamics}},
    journal =  {J. Nucl. Mater.},
    year =     2021,
    volume =   543,
    pages =    152542,
    doi =      {10.1016/j.jnucmat.2020.152542},
}
@Article{Liu_JNuclMater_2021_v545_p152733,
    author =   {Yu Liu and Xiaohui Liu and Mohan Chen},
    title =    {{Copper-doped beryllium and beryllium oxide interface: A first-
             principles study}},
    journal =  {J. Nucl. Mater.},
    year =     2021,
    volume =   545,
    pages =    152733,
    doi =      {10.1016/j.jnucmat.2020.152733},
}
@Article{Bakhsh_JPhysChemA_2021_v125_p1424,
    author =   {Sunila Bakhsh and Xiaohui Liu and Yanyong Wang and Lixin He and Xinguo
             Ren},
    title =    {{Beryllium and Magnesium Metal Clusters: New Globally Stable Structures
             and G0W0 Calculations}},
    journal =  {J. Phys. Chem., A},
    year =     2021,
    volume =   125,
    number =   7,
    pages =    {1424--1435},
    doi =      {10.1021/acs.jpca.0c08960},
    abstract = {We study the structural and electronic properties of beryllium (Be)
             and magnesium (Mg) clusters for sizes 2-20 using a two-step approach.
             In the first step, a global search of the stable and low-lying
             metastable isomer structures is carried out on the basis of first-
             principles potential energy surfaces at the level of the generalized
             gradient approximation (GGA) of density functional theory (DFT). In
             the second step, vertical ionization potentials (VIPs) and energy gaps
             between the highest occupied molecular orbital (HOMO) and lowest
             unoccupied molecular orbital (LUMO) are determined using the G0W0
             methods for up to the fourth-lowest-energy isomers. Novel globally
             lowest-energy isomer structures are identified for Be14, Mg14, and
             Mg16 clusters. The van der Waals interactions are found to have a
             stronger influence on Mg clusters than on Be clusters. A second-
             difference analysis for both the binding energies and HOMO-LUMO gaps
             reveals a close relationship between the structural stability and
             chemical hardness for both types of clusters.},
}
@Article{Wang_ChinPhysB_2021_v30_p107102,
    author =   {Qianpeng Wang and Daye Zheng and Lixin He and Xinguo Ren},
    title =    {{Peculiar diffusion behavior of AlCl4 intercalated in graphite from
             nanosecond-long molecular dynamics simulations*}},
    journal =  {Chin. Phys, B},
    year =     2021,
    volume =   30,
    number =   10,
    pages =    107102,
    doi =      {10.1088/1674-1056/ac0692},
    abstract = {The diffusion property of the intercalated species in the graphite
             materials is at the heart of the rate performance of graphite-based
             metal-ion secondary battery. Here we study the diffusion process of a
             AlCl4 molecule within graphite {\textemdash} a key component of a
             recently reported aluminum ion battery with excellent performance
             {\textemdash} via molecular dynamics (MD) simulations. Both ab-initio
             MD (AIMD) and semiempirical tight-binding MD simulations show that the
             diffusion process of the intercalated AlCl4 molecule becomes rather
             inhomogeneous, when the simulation time exceeds approximately 100
             picoseconds. Specifically, during its migration in between graphene
             layers, the intercalated AlCl4 molecule may become stagnant
             occasionally, and then recovers its normal (fast) diffusion behavior
             after halting for a while. When this phenomenon occurs, the linear
             relationship of the mean squared displacement (MSD) versus the
             duration time is not fulfilled. We interpret this peculiar behavior as
             a manifestation of inadequate sampling of rare event (the stagnation
             of the molecule), which does not yet appear in short-time MD
             simulations. We further check the influence of strains present in
             graphite intercalated compounds (GIC) on the diffusion properties of
             AlCl4, and find that their presence in general slows down the
             diffusion of the intercalated molecule, and is detrimental to the rate
             performance of the GIC-based battery.},
}
@Article{Zheng_ComputPhysCommun_2021_v267_p108043,
    author =   {Daye Zheng and Xinguo Ren and Lixin He},
    title =    {{Accurate stress calculations based on numerical atomic orbital bases:
             Implementation and benchmarks}},
    journal =  {Comput. Phys. Commun.},
    year =     2021,
    volume =   267,
    pages =    108043,
    doi =      {10.1016/j.cpc.2021.108043},
}
@Article{Lin_JChemTheoryComput_2021_v17_p222,
    author =   {Peize Lin and Xinguo Ren and Lixin He},
    title =    {{Efficient Hybrid Density Functional Calculations for Large Periodic
             Systems Using Numerical Atomic Orbitals}},
    journal =  {J. Chem. Theory Comput.},
    year =     2021,
    volume =   17,
    number =   1,
    pages =    {222--239},
    doi =      {10.1021/acs.jctc.0c00960},
    abstract = {We present an efficient, linear-scaling implementation for building
             the (screened) Hartree-Fock exchange (HFX) matrix for periodic systems
             within the framework of numerical atomic orbital (NAO) basis
             functions. Our implementation is based on the localized resolution of
             the identity approximation by which two-electron Coulomb repulsion
             integrals can be obtained by only computing two-center quantities-a
             feature that is highly beneficial to NAOs. By exploiting the locality
             of basis functions and efficient prescreening of the intermediate
             three- and two-index tensors, one can achieve a linear scaling of the
             computational cost for building the HFX matrix with respect to the
             system size. Our implementation is massively parallel, thanks to a
             MPI/OpenMP hybrid parallelization strategy for distributing the
             computational load and memory storage. All these factors add together
             to enable highly efficient hybrid functional calculations for large-
             scale periodic systems. In this work, we describe the key algorithms
             and implementation details for the HFX build as implemented in the
             ABACUS code package. The performance and scalability of our
             implementation with respect to the system size and the number of CPU
             cores are demonstrated for selected benchmark systems up to 4096
             atoms.},
}
@Article{Lin_PhysRevB_2021_v103_p235131,
    author =   {Peize Lin and Xinguo Ren and Lixin He},
    title =    {{Strategy for constructing compact numerical atomic orbital basis sets
             by incorporating the gradients of reference wavefunctions}},
    journal =  {Phys. Rev. B},
    year =     2021,
    volume =   103,
    number =   23,
    pages =    235131,
    doi =      {10.1103/PhysRevB.103.235131},
}
@Article{Yang_AdvMaterDeerfieldBeachFla_2021_v33_pe2106353,
    author =   {Hai Yang and Fuxiang He and Menghao Li and Fanyang Huang and Zhihao
             Chen and Pengcheng Shi and Fanfan Liu and Yu Jiang and Lixin He and
             Meng Gu and Yan Yu},
    title =    {{Design Principles of Sodium/Potassium Protection Layer for High-Power
             High-Energy Sodium/Potassium-Metal Batteries in Carbonate
             Electrolytes: a Case Study of Na2 Te/K2 Te}},
    journal =  {Adv. Mater. (Deerfield Beach Fla,)},
    year =     2021,
    volume =   33,
    number =   48,
    pages =    {e2106353},
    doi =      {10.1002/adma.202106353},
    abstract = {The sodium (potassium)-metal anodes combine low-cost, high theoretical
             capacity, and high energy density, demonstrating promising application
             in sodium (potassium)-metal batteries. However, the dendrites' growth
             on the surface of Na (K) has impeded their practical application.
             Herein, density functional theory (DFT) results predict Na2 Te/K2 Te
             is beneficial for Na+ /K+ transport and can effectively suppress the
             formation of the dendrites because of low Na+ /K+ migration energy
             barrier and ultrahigh Na+ /K+ diffusion coefficient of 3.7
             {\texttimes} 10-10 cm2 s-1 /1.6 {\texttimes} 10-10 cm2 s-1 (300 K),
             respectively. Then a Na2 Te protection layer is prepared by directly
             painting the nanosized Te powder onto the sodium-metal surface. The
             Na@Na2 Te anode can last for 700 h in low-cost carbonate electrolytes
             (1{~}mA cm-2 , 1 mAh cm-2 ), and the corresponding Na3 V2 (PO4 )3
             //Na@Na2 Te full cell exhibits high energy density of 223{~}Wh kg-1 at
             an unprecedented power density of 29687 W kg-1 as well as an ultrahigh
             capacity retention of 93{\%} after 3000 cycles at 20 C. Besides, the
             K@K2 Te-based potassium-metal full battery also demonstrates high
             power density of 20 577 W kg-1 with energy density of 154{~}Wh kg-1 .
             This work opens up a new and promising avenue to stabilize sodium
             (potassium)-metal anodes with simple and low-cost interfacial layers.},
}
@Article{Qu_JChemPhys_2022_v156_p234104,
    author =   {Xin Qu and Peng Xu and Hong Jiang and Lixin He and Xinguo Ren},
    title =    {{DFT+U within the framework of linear combination of numerical atomic
             orbitals}},
    journal =  {J. Chem. Phys.},
    year =     2022,
    volume =   156,
    number =   23,
    pages =    234104,
    doi =      {10.1063/5.0090122},
    abstract = {We present a formulation and implementation of the density functional
             theory (DFT)+U method within the framework of linear combination of
             numerical atomic orbitals (NAO). Our implementation not only enables
             single-point total energy and electronic-structure calculations but
             also provides access to atomic forces and cell stresses, hence
             allowing for full structure relaxations of periodic systems.
             Furthermore, our implementation allows one to deal with non-collinear
             spin texture, with the spin-orbit coupling (SOC) effect treated self-
             consistently. The key aspect behind our implementation is a suitable
             definition of the correlated subspace when multiple atomic orbitals
             with the same angular momentum are used, and this is addressed via the
             {''}Mulliken charge projector{''} constructed in terms of the first
             (most localized) atomic orbital within the d/f angular momentum
             channel. The important Hubbard U and Hund J parameters can be
             estimated from a screened Coulomb potential of the Yukawa type, with
             the screening parameter either chosen semi-empirically or determined
             from the Thomas-Fermi screening model. Benchmark calculations are
             performed for four late transition metal monoxide bulk systems, i.e.,
             MnO, FeO, CoO, and NiO, and for the 5d-electron compounds IrO2. For
             the former type of systems, we check the performance of our DFT+U
             implementation for calculating bandgaps, magnetic moments, electronic
             band structures, as well as forces and stresses; for the latter, the
             efficacy of our DFT+U+SOC implementation is assessed. Systematic
             comparisons with available experimental results, especially with the
             results from other implementation schemes, are carried out, which
             demonstrate the validity of our NAO-based DFT+U formalism and
             implementation.},
}
@Article{Qu_JChemTheoryComput_2022_v18_p5589,
    author =   {Xin Qu and Peng Xu and Rusong Li and Gang Li and Lixin He and Xinguo
             Ren},
    title =    {{Density Functional Theory Plus Dynamical Mean Field Theory within the
             Framework of Linear Combination of Numerical Atomic Orbitals:
             Formulation and Benchmarks}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume =   18,
    number =   9,
    pages =    {5589--5606},
    doi =      {10.1021/acs.jctc.2c00472},
    abstract = {The combination of density functional theory with dynamical mean-field
             theory (DFT+DMFT) has become a powerful first-principles approach to
             tackle strongly correlated materials in condensed matter physics. The
             wide use of this approach relies on robust and easy-to-use
             implementations, and its implementation in various numerical
             frameworks will increase its applicability on the one hand and help
             crosscheck the validity of the obtained results on the other. In this
             work, we develop a formalism within the linear combination of
             numerical atomic orbital (NAO) basis set framework, which allows for
             merging of NAO-based DFT codes with DMFT quantum impurity solvers. The
             formalism is implemented by interfacing two NAO-based DFT codes with
             three DMFT impurity solvers, and its validity is testified by
             benchmark calculations for a wide range of strongly correlated
             materials, including 3d transition metal compounds, lanthanides, and
             actinides. Our work not only enables DFT+DMFT calculations using
             popular and rapidly developing NAO-based DFT code packages but also
             facilitates the combination of more advanced beyond-DFT methodologies
             available in these codes with the DMFT machinery.},
}
@Article{Dai_ComputMaterSci_2022_v213_p111656,
    author =   {Zujian Dai and Gan Jin and Lixin He},
    title =    {{First-principles calculations of the surface states of doped and
             alloyed topological materials via band unfolding method}},
    journal =  {Comput. Mater. Sci.},
    year =     2022,
    volume =   213,
    pages =    111656,
    doi =      {10.1016/j.commatsci.2022.111656},
}
@Article{Chen_PhysRevB_2022_v106_p235427,
    author =   {Duyu Chen and Yu Liu and Yu Zheng and Houlong Zhuang and Mohan Chen
             and Yang Jiao},
    title =    {{Disordered hyperuniform quasi-one-dimensional materials}},
    journal =  {Phys. Rev. B},
    year =     2022,
    volume =   106,
    number =   23,
    pages =    235427,
    doi =      {10.1103/PhysRevB.106.235427},
    abstract = {Carbon nanotubes are quasi-one-dimensional systems that possess
             superior transport, mechanical, optical, and chemical properties. In
             this work, we generalize the notion of disorder hyperuniformity, a
             recently discovered exotic state of matter with hidden long-range
             order, to quasi-one-dimensional materials. As a proof of concept, we
             then apply the generalized framework to quantify the density
             {fl}uctuations in amorphous carbon nanotubes containing randomly
             distributed Stone-Wales defects. We demonstrate that all of these
             amorphous nanotubes are hyperuniform, i.e., the in{fi}nite-wavelength
             density {fl}uctuations of these systems are completely suppressed,
             regardless of the diameter, rolling axis, number of rolling sheets,
             and defect fraction of the nanotubes. We {fi}nd that these amorphous
             nanotubes are energetically more stable than nanotubes with
             periodically distributed Stone-Wales defects. Moreover, certain
             semiconducting defect-free carbon nanotubes become metallic as
             su{ffi}ciently large amounts of defects are randomly introduced. This
             structural study of amorphous nanotubes strengthens our fundamental
             understanding of these systems, and suggests possible exotic physical
             properties, as endowed by their disordered hyperuniformity. Our
             {fi}ndings also shed light on the e{ff}ect of dimensionality reduction
             on the hyperuniformity property of materials.},
}
@Article{Liu_PhysChemChemPhys_2022_v24_p15511,
    author =   {Yu Liu and Xinlong Ding and Mohan Chen and Shenzhen Xu},
    title =    {{A caveat of the charge-extrapolation scheme for modeling
             electrochemical reactions on semiconductor surfaces: an issue induced
             by a discontinuous Fermi level change}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2022,
    volume =   24,
    number =   25,
    pages =    {15511--15521},
    doi =      {10.1039/D2CP00642A},
    abstract = {The invalidity of the constant capacitance assumption in the charge-
             extrapolation scheme applied for semiconductor cases, and the improved
             results when the cross-bandgap feature of the Fermi level variation is
             avoided.},
}
@Article{He_JPhysChemLett_2022_v13_p427,
    author =   {Fuxiang He and Xinguo Ren and Jun Jiang and Guozhen Zhang and Lixin He},
    title =    {{Real-Time, Time-Dependent Density Functional Theory Study on
             Photoinduced Isomerizations of Azobenzene Under a Light Field}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2022,
    volume =   13,
    number =   2,
    pages =    {427--432},
    doi =      {10.1021/acs.jpclett.1c03442},
    abstract = {The trans to cis photoisomerization of azobenzene and its reverse
             (i.e., the cis to trans) processes are studied using real-time
             propagation time-dependent density functional theory combined with
             molecular dynamics for ions. We show that the wavelength of the
             applied laser may significantly affect the transition process. The
             simulations also show that the photon-excited electrons play essential
             roles in the isomerization processes, in which the hot electrons
             couple to phonon modes that drive the transitions.},
}
@Article{Ji_JPhysChemA_2022_v126_p5924,
    author =   {Yuyang Ji and Peize Lin and Xinguo Ren and Lixin He},
    title =    {{Reproducibility of Hybrid Density Functional Calculations for
             Equation-of-State Properties and Band Gaps}},
    journal =  {J. Phys. Chem., A},
    year =     2022,
    volume =   126,
    number =   35,
    pages =    {5924--5931},
    doi =      {10.1021/acs.jpca.2c05170},
    abstract = {Hybrid density functional (HDF) approximations usually deliver higher
             accuracy than local and semilocal approximations to the exchange-
             correlation functional, but this comes with drastically increased
             computational cost. Practical implementations of HDFs inevitably
             involve numerical approximations-even more so than their local and
             semilocal counterparts due to the additional numerical complexity
             arising from treating the exact-exchange component. This raises the
             question regarding the reproducibility of the HDF results yielded by
             different implementations. In this work, we benchmark the numerical
             precision of four independent implementations of the popular Heyd-
             Scuseria-Ernzerhof (HSE) range-separated HDF on describing key
             materials' properties, including both properties derived from
             equations of state (EOS) and band gaps of 20 crystalline solids. We
             find that the energy band gaps obtained by the four codes agree with
             each other rather satisfactorily. However, for lattice constants and
             bulk moduli, the deviations between the results computed by different
             codes are of the same order of magnitude as the deviations between the
             computational and experimental results. On the one hand, this means
             that the HSE functional is rather accurate for describing the cohesive
             properties of simple insulating solids. On the other hand, this also
             suggests that the numerical precision achieved with current major HSE
             implementations is not sufficiently high to unambiguously assess the
             physical accuracy of HDFs. It is found that the pseudopotential
             treatment of the core electrons is a major factor that contributes to
             this uncertainty.},
}
@Article{Liu_JChemPhys_2023_v159_p074109,
    author =   {Renxi Liu and Daye Zheng and Xinyuan Liang and Xinguo Ren and Mohan
             Chen and Wenfei Li},
    title =    {{Implementation of the meta-GGA exchange-correlation functional in
             numerical atomic orbital basis: With systematic testing on SCAN,
             rSCAN, and r2SCAN functionals}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    number =   7,
    pages =    074109,
    doi =      {10.1063/5.0160726},
    abstract = {Kohn-Sham density functional theory (DFT) is nowadays widely used for
             electronic structure theory simulations, and the accuracy and
             efficiency of DFT rely on approximations of the exchange-correlation
             functional. By including the kinetic energy density
             {\ensuremath{\tau}}, the meta-generalized-gradient approximation
             (meta-GGA) family of functionals achieves better accuracy and
             flexibility while retaining the efficiency of semi-local functionals.
             For example, the strongly constrained and appropriately normed (SCAN)
             meta-GGA functional has been proven to yield accurate results for
             solid and molecular systems. We implement meta-GGA functionals with
             both numerical atomic orbitals and plane wave bases in the ABACUS
             package. Apart from the exchange-correlation potential, we also
             discuss the evaluation of force and stress. To validate our
             implementation, we perform finite-difference tests and convergence
             tests with the SCAN, rSCAN, and r2SCAN meta-GGA functionals. We
             further test water hexamers, weakly interacting molecules from the S22
             dataset, as well as 13 semiconductors using the three functionals. The
             results show satisfactory agreement with previous calculations and
             available experimental values.},
}
@Article{Dai_PhysRevB_2023_v108_p85112,
    author =   {Zujian Dai and Gan Jin and Lixin He},
    title =    {{Interplay between magnetic structures and surface states in  MnBi2Te4
             from first-principles studies}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   108,
    number =   8,
    pages =    85112,
    doi =      {10.1103/PhysRevB.108.085112},
}
@Article{Jin_ComputPhysCommun_2023_v291_p108844,
    author =   {Gan Jin and Hongsheng Pang and Yuyang Ji and Zujian Dai and Lixin He},
    title =    {{PYATB: An efficient Python package for electronic structure
             calculations using ab initio tight-binding model}},
    journal =  {Comput. Phys. Commun.},
    year =     2023,
    volume =   291,
    pages =    108844,
    doi =      {10.1016/j.cpc.2023.108844},
}
@Article{He_PhysRevRes_2024_v6_p13123,
    author =   {Fuxiang He and Daqiang Chen and Xinguo Ren and Sheng Meng and Lixin He},
    title =    {{Ultrafast shift current dynamics in  WS2  monolayer}},
    journal =  {Phys, Rev, Res.},
    year =     2024,
    volume =   6,
    number =   1,
    pages =    13123,
    doi =      {10.1103/PhysRevResearch.6.013123},
    abstract = {The shift current effect, in materials lacking inversion symmetry, may
             potentially allow the performance of photovoltaics to surpass the
             Shockley-Queisser limit for traditional p{\ensuremath{-}}n junction-
             based photovoltaics. Although the shift-current effect has been
             studied from first principles via second-order perturbation theory, an
             understanding of the dynamics of hot carriers is still lacking. We
             investigate the dynamics of the shift current in monolayer WS2 via
             real-time propagation time-dependent density functional theory (rt-
             TDDFT). We find that the shift current can be generated within
             10{\textendash}20 fs after turning on the lights, and dissipates
             within approximately a few tens of femtoseconds after turning off the
             lights. This property can be used for ultrafast photon detection. This
             work provides an important step toward understanding the dynamics of
             shift-current effects, which is crucial for device applications.
             Published by the American Physical Society                 2024},
}
@Article{Lin_WiresComputMolSci_2024_v14,
    author =   {Peize Lin and Xinguo Ren and Xiaohui Liu and Lixin He},
    title =    {{Ab initio electronic structure calculations based on numerical atomic
             orbitals: Basic fomalisms and recent progresses}},
    journal =  {Wires Comput. Mol Sci},
    year =     2024,
    volume =   14,
    number =   1,
    doi =      {10.1002/wcms.1687},
    abstract = {AbstractThe numerical atomic orbital (NAO) basis sets offer a
             computationally efficient option for electronic structure
             calculations, as they require fewer basis functions compared with
             other types of basis sets. Moreover, their strict localization allows
             for easy combination with current linear scaling methods, enabling
             efficient calculation of large physical systems. In recent years, NAO
             bases have become increasingly popular in modern electronic structure
             codes. This article provides a review of the ab initio electronic
             structure calculations using NAO bases. We begin by introducing basic
             formalisms of the NAO{-}based electronic structure method, including
             NAO base set generation, self{-}consistent calculations, force, and
             stress calculations. We will then discuss some recent advances in the
             methods based on the NAO bases, such as real{-}time dependent density
             functional theory (rt{-}TDDFT), efficient implementation of hybrid
             functionals, and other advanced electronic structure methods. Finally,
             we introduce the ab initio tight{-}binding model, which can be
             generated directly after the self{-}consistent calculations. The model
             allows for efficient calculation of electronic structures, and the
             associated topological, and optical properties of the systems.This
             article is categorized under: Electronic Structure Theory {\&}gt; Ab
             Initio Electronic Structure Methods Electronic Structure Theory
             {\&}gt; Density Functional Theory Structure and Mechanism {\&}gt;
             Computational Materials Science},
}
@Article{Yang_AdvMaterDeerfieldBeachFla_2024_v36_pe2306512,
    author =   {Hai Yang and Fuxiang He and Fanfan Liu and Zhefei Sun and Yu Shao and
             Lixin He and Qiaobao Zhang and Yan Yu},
    title =    {{Simultaneous Catalytic Acceleration of White Phosphorus Polymerization
             and Red Phosphorus Potassiation for High-Performance Potassium-Ion
             Batteries}},
    journal =  {Adv. Mater. (Deerfield Beach Fla,)},
    year =     2024,
    volume =   36,
    number =   3,
    pages =    {e2306512},
    doi =      {10.1002/adma.202306512},
    abstract = {Red phosphorus (P) as an anode material of potassium-ion batteries
             possesses ultra-high theoretical specific capacity (1154{~}mAh{~}g-1
             ). However, owing to residual white P during the preparation and
             sluggish kinetics of K-P alloying limit its practical application.
             Seeking an efficient catalyst to address the above problems is crucial
             for the secure preparation of red P anode with high performance.
             Herein, through the analysis of the activation energies in white P
             polymerization, it is revealed that the highest occupied molecular
             orbital energy of I2 (-7.40{~}eV) is in proximity to P4 (-7.25{~}eV),
             and the lowest unoccupied molecular orbital energy of I2 molecule
             (-4.20{~}eV) is lower than that of other common non-metallic molecules
             (N2 , S8 , Se8 , F2 , Cl2 , Br2 ). The introduction of I2 can thus
             promote the breaking of the P-P bond and accelerate the polymerization
             of white P molecules. Besides, the ab initio molecular dynamics
             simulations show that I2 can enhance the kinetics of P-K alloying. The
             as-obtained red P/C composites with I2 deliver excellent cycling
             stability (358{~}mAh{~}g-1 after 1200 cycles at 1{~}A{~}g-1 ). This
             study establishes catalysis as a promising pathway to tackle the
             challenges of P anode for alkali metal ion batteries.},
}
@Article{Jin_npjComputMater_2024_v10_p23,
    author =   {Gan Jin and Lixin He},
    title =    {{Peculiar band geometry induced giant shift current in ferroelectric
             SnTe monolayer}},
    journal =  {npj Comput. Mater},
    year =     2024,
    volume =   10,
    number =   1,
    pages =    23,
    doi =      {10.1038/s41524-024-01213-w},
    abstract = {AbstractThe bulk photovoltaic effect (BPVE) occurs when homogeneous
             noncentrosymmetric materials generate photocurrent or photovoltage
             under illumination. The intrinsic contribution to this effect is known
             as the shift current effect. We calculate the shift current
             conductivities of the ferroelectric SnTe monolayer using first-
             principles methods. Our results reveal a giant shift-current
             conductivity near the valley points in the SnTe monolayer. More
             remarkably, the linear optical absorption coefficient at this energy
             is very small, resulting in an enormous Glass coefficient that is four
             orders of magnitude larger than that of BaTiO3. To understand these
             giant shift-current effects, we employ a three-band model and find
             that they arise from the nontrivial energy band geometries near the
             valley points, where the shift-vector diverges. This serves as a
             prominent example highlighting the crucial role of band geometry in
             determining the fundamental properties of solids.},
}
@Article{Pang_PhysRevMater_2024_v8_p43403,
    author =   {Hongsheng Pang and Gan Jin and Lixin He},
    title =    {{Tuning of Berry-curvature dipole in TaAs slabs: An effective route to
             enhance the nonlinear Hall response}},
    journal =  {Phys, Rev, Mater.},
    year =     2024,
    volume =   8,
    number =   4,
    pages =    43403,
    doi =      {10.1103/PhysRevMaterials.8.043403},
    abstract = {In materials without inversion symmetry, Berry curvature dipole (BCD)
             arises from the uneven distribution of Berry curvature in momentum
             space. This leads to nonlinear anomalous Hall effects even in systems
             with preserved time-reversal symmetry. A key goal is to engineer
             systems with prominent BCD near the Fermi level. Notably, TaAs, a
             type-I Weyl semimetal, exhibits substantial Berry curvature but a
             small BCD around the Fermi level. In this study, we employed first-
             principles methods to comprehensively investigate the BCD in TaAs. Our
             findings reveal significant cancellation effects not only within
             individual Weyl points but crucially, among distinct Weyl point pairs
             in bulk TaAs. We propose a strategic approach to enhance the BCD in
             TaAs by employing a layer-stacking technique. This greatly amplifies
             the BCD compared to the bulk material. By tuning the number of slab
             layers, we can selectively target specific Weyl point pairs near the
             Fermi level, while quantum confinement effects suppress contributions
             from other pairs, mitigating cancellation effects. Especially, the BCD
             of an 8-layer TaAs slab surpasses the bulk value near the Fermi level
             by orders of magnitude.},
}
@Article{Zhao_NanoLett_2024_v24_p5513,
    author =   {Zhenzhu Zhao and Mulin Sun and Yuyang Ji and Kaitian Mao and Zongming
             Huang and Chengjian Yuan and Yuqian Yang and Honghe Ding and Yingguo
             Yang and Yu Li and Wenjing Chen and Junfa Zhu and Jing Wei and Jixian
             Xu and Watcharaphol Paritmongkol and Antonio Abate and Zhengguo Xiao
             and Lixin He and Qin Hu},
    title =    {{Efficient Homojunction Tin Perovskite Solar Cells Enabled by Gradient
             Germanium Doping}},
    journal =  {Nano Lett.},
    year =     2024,
    volume =   24,
    number =   18,
    pages =    {5513--5520},
    doi =      {10.1021/acs.nanolett.4c00646},
    abstract = {P-type self-doping is known to hamper tin-based perovskites for
             developing high-performance solar cells by increasing the background
             current density and carrier recombination processes. In this work, we
             propose a gradient homojunction structure with germanium doping that
             generates an internal electric field across the perovskite film to
             deplete the charge carriers. This structure reduces the dark current
             density of perovskite by over 2 orders of magnitude and trap density
             by an order of magnitude. The resultant tin-based perovskite solar
             cells exhibit a higher power conversion efficiency of 13.3{\%} and
             excellent stability, maintaining 95{\%} and 85{\%} of their initial
             efficiencies after 250 min of continuous illumination and 3800 h of
             storage, respectively. We reveal the homojunction formation mechanism
             using density functional theory calculations and molecular level
             characterizations. Our work provides a reliable strategy for
             controlling the spatial energy levels in tin perovskite films and
             offers insights into designing intriguing lead-free perovskite
             optoelectronics.},
}
@Article{Achar_JPhysChemC_2021_v125_p14874,
    author =   {Siddarth K. Achar and Linfeng Zhang and J. Karl Johnson},
    title =    {{Efficiently Trained Deep Learning Potential for Graphane}},
    journal =  {J. Phys. Chem. C},
    year =     2021,
    volume =   125,
    issue =    27,
    pages =    {14874--14882},
    doi =      {10.1021/acs.jpcc.1c01411},
    image = {https://pubs.acs.org/cms/10.1021/acs.jpcc.1c01411/asset/images/medium/jp1c01411_0012.gif},
}
@Article{Bonati_PhysRevLett_2018_v121_p265701,
    author =   {Luigi Bonati and Michele Parrinello},
    title =    {{Silicon Liquid Structure and Crystal Nucleation from
             <i>Ab{~}Initio</i>  Deep Metadynamics}},
    journal =  {Phys. Rev. Lett.},
    year =     2018,
    volume =   121,
    issue =    26,
    pages =    265701,
    annote =   {Studying the crystallization process of silicon is a challenging task
             since empirical potentials are not able to reproduce well the
             properties of both a semiconducting solid and metallic liquid. On the
             other hand, nucleation is a rare event that occurs in much longer
             timescales than those achievable by ab{~}initio molecular dynamics. To
             address this problem, we train a deep neural network potential based
             on a set of data generated by metadynamics simulations using a
             classical potential. We show how this is an effective way to collect
             all the relevant data for the process of interest. In order to
             efficiently drive the crystallization process, we introduce a new
             collective variable based on the Debye structure factor. We are able
             to encode the long-range order information in a local variable which
             is better suited to describe the nucleation dynamics. The reference
             energies are then calculated using the strongly constrained and
             appropriately normed (SCAN) exchange-correlation functional, which is
             able to get a better description of the bonding complexity of the Si
             phase diagram. Finally, we recover the free energy surface with a
             density functional theory accuracy, and we compute the thermodynamics
             properties near the melting point, obtaining a good agreement with
             experimental data. In addition, we study the early stages of the
             crystallization process, unveiling features of the nucleation
             mechanism.},
    doi =      {10.1103/PhysRevLett.121.265701},
}
@Article{CalegariAndrade_ChemSci_2020_v11_p2335,
    author =   {Marcos F {Calegari Andrade} and Hsin-Yu Ko and Linfeng Zhang and
             Roberto Car and Annabella Selloni},
    title =    {{Free energy of proton transfer at the
             water{-}TiO<sub>2</sub> interface from <i>ab initio</i> deep
             potential molecular dynamics}},
    journal =  {Chem. Sci.},
    year =     2020,
    volume =   11,
    issue =    9,
    pages =    {2335--2341},
    annote =   {TiO2 is a widely used photocatalyst in science and technology and its
             interface with water is important in fields ranging from geochemistry
             to biomedicine. Yet, it is still unclear whether water adsorbs in
             molecular or dissociated form on TiO2 even for the case of well-
             defined crystalline surfaces. To address this issue, we simulated the
             TiO2-water interface using molecular dynamics with an ab initio-based
             deep neural network potential. Our simulations show a dynamical
             equilibrium of molecular and dissociative adsorption of water on TiO2.
             Water dissociates through a solvent-assisted concerted proton transfer
             to form a pair of short-lived hydroxyl groups on the TiO2 surface.
             Molecular adsorption of water is {\ensuremath{\Delta}}F = 8.0
             {\ensuremath{\pm}} 0.9 kJ mol-1 lower in free energy than the
             dissociative adsorption, giving rise to a 5.6 {\ensuremath{\pm}} 0.5%
             equilibrium water dissociation fraction at room temperature. Due to
             the relevance of surface hydroxyl groups to the surface chemistry of
             TiO2, our model might be key to understanding phenomena ranging from
             surface functionalization to photocatalytic mechanisms.},
    PMCID =    {PMC8157430},
    doi =      {10.1039/C9SC05116C},
}
@Article{Chen_ACSNano_2021_v15_p12418,
    author =   {Huayu Chen and Junxiang Chen and Pei Ning and Xin Chen and Junhui
             Liang and Xin Yao and Da Chen and Laishun Qin and Yuexiang Huang and
             Zhenhai Wen},
    title =    {{2D Heterostructure of Amorphous CoFeB Coating Black Phosphorus
             Nanosheets with Optimal Oxygen Intermediate Absorption for Improved
             Electrocatalytic Water Oxidation}},
    journal =  {ACS Nano},
    year =     2021,
    volume =   15,
    issue =    7,
    pages =    {12418--12428},
    annote =   {The oxygen evolution reaction (OER) plays a paramount role in a
             variety of electrochemical energy conversion devices, and the
             exploration of highly active, stable, and low-cost electrocatalysts is
             one of the most important topics in this field. The exfoliated black
             phosphorus (EBP) nanosheet with a two-dimensional (2D) layered
             structure has high carrier mobility but is limited by excessive
             oxygen-containing intermediate absorption and fast deterioration in
             air. We here report the fabrication of nanohybrids of amorphous CoFeB
             nanosheets on EBP nanosheets (EBP/CoFeB). The 2D/2D heterostructure,
             thanks to the electronic interactions and oxygen affinity difference
             between EBP and CoFeB nanosheets, is capable of balancing the oxygen-
             containing intermediate absorption to an optimal status for
             facilitating the OER process. While the crystalline EBP contributes to
             the improved conductivity, the amorphous coating protects EBP and thus
             ensures the catalytic stability. The EBP/CoFeB electrocatalyst shows
             excellent OER performance with an ultralow overpotential of 227 mV at
             10 mA cm-2 with an ultrasmall Tafel slope of 36.7 mV dec-1 with
             excellent stability. This study may inspire more researches to develop
             heterostructured nanohybrid electrocatalysts for a diversity of
             electrochemical reactions.},
    doi =      {10.1021/acsnano.1c04715},
    image = {https://pubs.acs.org/cms/10.1021/acsnano.1c04715/asset/images/medium/nn1c04715_0004.gif},
}
@Article{Chen_JPhysChemLett_2018_v9_p6702,
    author =   {Wen-Kai Chen and Xiang-Yang Liu and Wei-Hai Fang and Pavlo O Dral and
             Ganglong Cui},
    title =    {{Deep Learning for Nonadiabatic Excited-State Dynamics}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2018,
    volume =   9,
    issue =    23,
    pages =    {6702--6708},
    annote =   {In this work we show that deep learning (DL) can be used for exploring
             complex and highly nonlinear multistate potential energy surfaces of
             polyatomic molecules and related nonadiabatic dynamics. Our DL is
             based on deep neural networks (DNNs), which are used as accurate
             representations of the CASSCF ground- and excited-state potential
             energy surfaces (PESs) of CH2NH. After geometries near conical
             intersection are included in the training set, the DNN models
             accurately reproduce excited-state topological structures;
             photoisomerization paths; and, importantly, conical intersections. We
             have also demonstrated that the results from nonadiabatic dynamics run
             with the DNN models are very close to those from the dynamics run with
             the pure ab initio method. The present work should encourage further
             studies of using machine learning methods to explore excited-state
             potential energy surfaces and nonadiabatic dynamics of polyatomic
             molecules.},
    doi =      {10.1021/acs.jpclett.8b03026},
    image = {https://pubs.acs.org/cms/10.1021/acs.jpclett.8b03026/asset/images/medium/jz-2018-03026r_0008.gif},
}
@Article{Dai_JournalofMaterialsScienceTechnology_2020_v43_p168,
    author =   {Fu-Zhi Dai and Bo Wen and Yinjie Sun and Huimin Xiang and Yanchun Zhou},
    title =    {{Theoretical prediction on thermal and mechanical properties of high
             entropy (Zr<sub>0.2</sub>Hf<sub>0.2</sub>Ti<sub>0.2</sub>Nb<sub>0.2</sub>Ta<sub>0.2</sub>)C by deep learning potential}},
    journal =  {Journal of Materials Science &amp; Technology},
    year =     2020,
    volume =   43,
    pages =    {168--174},
    doi =      {10.1016/j.jmst.2020.01.005},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S1005030220300050-gr1.jpg},
}
@Article{Ding_MaterialsScienceinSemiconductorProcessing_2022_v143_p106513,
    author =   {Xi Ding and Ming Tao and Junhua Li and Mingyuan Li and Mengchao Shi
             and Jiashu Chen and Zhen Tang and Francis Benistant and Jie Liu},
    title =    {{Efficient and accurate atomistic modeling of dopant migration using
             deep neural network}},
    journal =  {Materials Science in Semiconductor Processing},
    year =     2022,
    volume =   143,
    pages =    106513,
    doi =      {10.1016/j.mssp.2022.106513},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S1369800122000610-gr1.jpg},
}
@Article{Huang_JChemPhys_2021_v154_p094703,
    author =   {Jianxing Huang and Linfeng Zhang and Han Wang and Jinbao Zhao and Jun
             Cheng and Weinan E},
    title =    {{Deep potential generation scheme and simulation protocol for the
             Li<sub>10</sub>GeP<sub>2</sub>S<sub>12</sub>-type superionic
             conductors}},
    journal =  {J. Chem. Phys.},
    year =     2021,
    volume =   154,
    issue =    9,
    pages =    094703,
    annote =   {Solid-state electrolyte materials with superior lithium ionic
             conductivities are vital to the next-generation Li-ion batteries.
             Molecular dynamics could provide atomic scale information to
             understand the diffusion process of Li-ion in these superionic
             conductor materials. Here, we implement the deep potential generator
             to set up an efficient protocol to automatically generate interatomic
             potentials for Li10GeP2S12-type solid-state electrolyte materials
             (Li10GeP2S12, Li10SiP2S12, and Li10SnP2S12). The reliability and
             accuracy of the fast interatomic potentials are validated. With the
             potentials, we extend the simulation of the diffusion process to a
             wide temperature range (300 K-1000 K) and systems with large size
             ({\ensuremath{\sim}}1000 atoms). Important technical aspects such as
             the statistical error and size effect are carefully investigated, and
             benchmark tests including the effect of density functional, thermal
             expansion, and configurational disorder are performed. The computed
             data that consider these factors agree well with the experimental
             results, and we find that the three structures show different
             behaviors with respect to configurational disorder. Our work paves the
             way for further research on computation screening of solid-state
             electrolyte materials.},
    doi =      {10.1063/5.0041849},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/jcp/2021/jcp.2021.154.issue-9/5.0041849/20210228/images/small/5.0041849.figures.online.f1.gif},
}
@Article{Jiao_AdvSci_2022_v9_pe2105574,
    author =   {Junyu Jiao and Genming Lai and Liang Zhao and Jiaze Lu and Qidong Li
             and Xianqi Xu and Yao Jiang and Yan-Bing He and Chuying Ouyang and
             Feng Pan and Hong Li and Jiaxin Zheng},
    title =    {{Self{-}Healing Mechanism of Lithium in Lithium Metal}},
    journal =  {Adv. Sci. (Weinh).},
    year =     2022,
    volume =   9,
    issue =    12,
    pages =    {e2105574},
    annote =   {Li is an ideal anode material for use in state-of-the-art secondary
             batteries. However, Li-dendrite growth is a safety concern and results
             in low coulombic efficiency, which significantly restricts the
             commercial application of Li secondary batteries. Unfortunately, the
             Li-deposition (growth) mechanism is poorly understood on the atomic
             scale. Here, machine learning is used to construct a Li potential
             model with quantum-mechanical computational accuracy. Molecular
             dynamics simulations in this study with this model reveal two self-
             healing mechanisms in a large Li-metal system, viz. surface self-
             healing, and bulk self-healing. It is concluded that self-healing
             occurs rapidly in nanoscale; thus, minimizing the voids between the Li
             grains using several comprehensive methods can effectively facilitate
             the formation of dendrite-free Li.},
    PMCID =    {PMC9036043},
    doi =      {10.1002/advs.202105574},
    image = {https://onlinelibrary.wiley.com/cms/asset/0d3b53e9-52aa-4247-bdee-c1b6d5a70854/advs3655-fig-0001-m.png},
}
@Article{Li_ApplPhysLett_2020_v117_p152102,
    author =   {Ruiyang Li and Zeyu Liu and Andrew Rohskopf and Kiarash Gordiz and
             Asegun Henry and Eungkyu Lee and Tengfei Luo},
    title =    {{A deep neural network interatomic potential for studying thermal
             conductivity of
             <b><i>{${\beta}$}</i></b>-Ga<sub>2</sub>O<sub>3</sub>}},
    journal =  {Appl. Phys. Lett.},
    year =     2020,
    volume =   117,
    issue =    15,
    pages =    152102,
    doi =      {10.1063/5.0025051},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/apl/2020/apl.2020.117.issue-15/5.0025051/20201014/images/medium/5.0025051.figures.online.f1.jpg},
}
@Article{Liu_JPhysCondensMatter_2020_v32_p144002,
    author =   {Qianrui Liu and Denghui Lu and Mohan Chen},
    title =    {{Structure and dynamics of warm dense aluminum: a molecular dynamics
             study with density functional theory and deep potential}},
    journal =  {J. Phys. Condens. Matter},
    year =     2020,
    volume =   32,
    issue =    14,
    pages =    144002,
    annote =   {We perform a systematic study on the structure and dynamics of warm
             dense aluminum (Al) at temperatures ranging from 0.5 to 5.0{\,}eV with
             molecular dynamics utilizing both density functional theory (DFT) and
             the deep potential (DP) method. On one hand, unlike the Thomas-Fermi
             kinetic energy density functional (KEDF), we find that the orbital-
             free DFT method with the Wang-Teter non-local KEDF yields properties
             of warm dense Al that agree well with the Kohn-Sham DFT method,
             enabling accurate orbital-free DFT simulations of warm dense Al at
             relatively low temperatures. On the other hand, the DP method
             constructs a deep neural network that has a high accuracy in
             reproducing short- and long-ranged properties of warm dense Al when
             compared to the DFT methods. The DP method is orders of magnitudes
             faster than DFT and is well-suited for simulating large systems and
             long trajectories to yield accurate properties of warm dense Al. Our
             results suggest that the combination of DFT methods and the DP model
             is a powerful tool for accurately and efficiently simulating warm
             dense matter.},
    doi =      {10.1088/1361-648X/ab5890},
    image = {https://cfn-live-content-bucket-iop-org.s3.amazonaws.com/journals/0953-8984/32/14/144002/revision2/cmab5890f01_online.jpg?AWSAccessKeyId=AKIAYDKQL6LTV7YY2HIK&Expires=1652065965&Signature=FwU20SWiIM9sQRNnDUSjVHvA7dw%3D},
}
@Article{Niu_NatCommun_2020_v11_p2654,
    author =   {Haiyang Niu and Luigi Bonati and Pablo M Piaggi and Michele Parrinello},
    title =    {{Ab initio phase diagram and nucleation of gallium}},
    journal =  {Nat. Commun.},
    year =     2020,
    volume =   11,
    issue =    1,
    pages =    2654,
    annote =   {Elemental gallium possesses several intriguing properties, such as a
             low melting point, a density anomaly and an electronic structure in
             which covalent and metallic features coexist. In order to simulate
             this complex system, we construct an ab initio quality interaction
             potential by training a neural network on a set of density functional
             theory calculations performed on configurations generated in
             multithermal-multibaric simulations. Here we show that the relative
             equilibrium between liquid gallium, {\ensuremath{\alpha}}-Ga,
             {\ensuremath{\beta}}-Ga, and Ga-II is well described. The resulting
             phase diagram is in agreement with the experimental findings. The
             local structure of liquid gallium and its nucleation into
             {\ensuremath{\alpha}}-Ga and {\ensuremath{\beta}}-Ga are studied. We
             find that the formation of metastable {\ensuremath{\beta}}-Ga is
             kinetically favored over the thermodinamically stable
             {\ensuremath{\alpha}}-Ga. Finally, we provide insight into the
             experimental observations of extreme undercooling of liquid Ga.},
    PMCID =    {PMC7253470},
    doi =      {10.1038/s41467-020-16372-9},
}
@Article{Pan_JChemTheoryComput_2021_v17_p5745,
    author =   {Xiaoliang Pan and Junjie Yang and Richard Van and Evgeny Epifanovsky
             and Junming Ho and Jing Huang and Jingzhi Pu and Ye Mei and Kwangho
             Nam and Yihan Shao},
    title =    {{Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and
             Enzyme Reactions}},
    journal =  {J. Chem. Theory Comput.},
    year =     2021,
    volume =   17,
    issue =    9,
    pages =    {5745--5758},
    annote =   {Despite recent advances in the development of machine learning
             potentials (MLPs) for biomolecular simulations, there has been limited
             effort on developing stable and accurate MLPs for enzymatic reactions.
             Here we report a protocol for performing machine-learning-assisted
             free energy simulation of solution-phase and enzyme reactions at the
             ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of
             accuracy. Within our protocol, the MLP is built to reproduce the ai-
             QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme)
             atoms. As an alternative strategy, a delta machine learning potential
             ({\ensuremath{\Delta}}MLP) is trained to reproduce the differences
             between the ai-QM/MM and semiempirical (se) QM/MM energies and forces.
             To account for the effect of the condensed-phase environment in both
             MLP and {\ensuremath{\Delta}}MLP, the DeePMD representation of a
             molecular system is extended to incorporate the external electrostatic
             potential and field on each QM atom. Using the Menshutkin and
             chorismate mutase reactions as examples, we show that the developed
             MLP and {\ensuremath{\Delta}}MLP reproduce the ai-QM/MM energy and
             forces with errors that on average are less than 1.0 kcal/mol and 1.0
             kcal mol-1 {\r{A}}-1, respectively, for representative configurations
             along the reaction pathway. For both reactions,
             MLP/{\ensuremath{\Delta}}MLP-based simulations yielded free energy
             profiles that differed by less than 1.0{\,}kcal/mol from the reference
             ai-QM/MM results at only a fraction of the computational cost.},
    doi =      {10.1021/acs.jctc.1c00565},
    image = {https://pubs.acs.org/cms/10.1021/acs.jctc.1c00201/asset/images/medium/ct1c00201_0008.gif},
}
@Article{Sommers_PhysChemChemPhys_2020_v22_p10592,
    author =   {Grace M {Sommers                                  } and Marcos F
             {Calegari Andrade                                  } and Linfeng
             {Zhang                                  } and Han {Wang
             } and Roberto {Car                                  }},
    title =    {{Raman spectrum and polarizability of liquid water from deep neural
             networks}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2020,
    volume =   22,
    issue =    19,
    pages =    {10592--10602},
    annote =   {We introduce a scheme based on machine learning and deep neural
             networks to model the environmental dependence of the electronic
             polarizability in insulating materials. Application to liquid water
             shows that training the network with a relatively small number of
             molecular configurations is sufficient to predict the polarizability
             of arbitrary liquid configurations in close agreement with ab initio
             density functional theory calculations. In combination with a neural
             network representation of the interatomic potential energy surface,
             the scheme allows us to calculate the Raman spectra along 2-nanosecond
             classical trajectories at different temperatures for H2O and D2O. The
             vast gains in efficiency provided by the machine learning approach
             enable longer trajectories and larger system sizes relative to ab
             initio methods, reducing the statistical error and improving the
             resolution of the low-frequency Raman spectra. Decomposing the spectra
             into intramolecular and intermolecular contributions elucidates the
             mechanisms behind the temperature dependence of the low-frequency and
             stretch modes.},
    doi =      {10.1039/D0CP01893G},
}
@Article{Tuo_JChemPhys_2020_v152_p114105,
    author =   {P Tuo and X B Ye and B C Pan},
    title =    {{A machine learning based deep potential for seeking the low-lying
             candidates of Al clusters}},
    journal =  {J. Chem. Phys.},
    year =     2020,
    volume =   152,
    issue =    11,
    pages =    114105,
    annote =   {A Machine-Learning based Deep Potential (DP) model for Al clusters is
             developed through training with an extended database including ab
             initio data of both bulk and several clusters in only 6 CPU/h. This DP
             model has good performance in accurately predicting the low-lying
             candidates of Al clusters in a broad size range. Based on our
             developed DP model, the low-lying structures of 101 different sized Al
             clusters are extensively searched, among which the lowest-energy
             candidates of 69 sized clusters are updated. Our calculations
             demonstrate that machine-learning is indeed powerful in generating
             potentials to describe the interaction of atoms in complex materials.},
    doi =      {10.1063/5.0001491},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/jcp/2020/jcp.2020.152.issue-11/5.0001491/20200314/images/medium/5.0001491.figures.online.f1.jpg},
}
@Article{Wang_Carbon_2022_v186_p1,
    author =   {Jinjin Wang and Hong Shen and Riyi Yang and Kun Xie and Chao Zhang and
             Liangyao Chen and Kai-Ming Ho and Cai-Zhuang Wang and Songyou Wang},
    title =    {{A deep learning interatomic potential developed for atomistic
             simulation of carbon materials}},
    journal =  {Carbon},
    year =     2022,
    volume =   186,
    pages =    {1--8},
    doi =      {10.1016/j.carbon.2021.09.062},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S0008622321009635-ga1.jpg},
}
@Article{Wang_SoftMatter_2020_v16_p8330,
    author =   {Shu Wang and Zhan Ma and Wenxiao Pan},
    title =    {{Data-driven coarse-grained modeling of polymers in solution with
             structural and dynamic properties conserved}},
    journal =  {Soft Matter},
    year =     2020,
    volume =   16,
    issue =    36,
    pages =    {8330--8344},
    annote =   {We present data-driven coarse-grained (CG) modeling for polymers in
             solution, which conserves the dynamic as well as structural properties
             of the underlying atomistic system. The CG modeling is built upon the
             framework of the generalized Langevin equation (GLE). The key is to
             determine each term in the GLE by directly linking it to atomistic
             data. In particular, we propose a two-stage Gaussian process-based
             Bayesian optimization method to infer the non-Markovian memory kernel
             from the data of the velocity autocorrelation function (VACF).
             Considering that the long-time behaviors of the VACF and memory kernel
             for polymer solutions can exhibit hydrodynamic scaling (algebraic
             decay with time), we further develop an active learning method to
             determine the emergence of hydrodynamic scaling, which can accelerate
             the inference process of the memory kernel. The proposed methods do
             not rely on how the mean force or CG potential in the GLE is
             constructed. Thus, we also compare two methods for constructing the CG
             potential: a deep learning method and the iterative Boltzmann
             inversion method. With the memory kernel and CG potential determined,
             the GLE is mapped onto an extended Markovian process to circumvent the
             expensive cost of directly solving the GLE. The accuracy and
             computational efficiency of the proposed CG modeling are assessed in a
             model star-polymer solution system at three representative
             concentrations. By comparing with the reference atomistic simulation
             results, we demonstrate that the proposed CG modeling can robustly and
             accurately reproduce the dynamic and structural properties of polymers
             in solution.},
    doi =      {10.1039/D0SM01019G},
}
@Article{Wu_PhysRevB_2021_v104_p174107,
    author =   {Jing Wu and Liyi Bai and Jiawei Huang and Liyang Ma and Jian Liu and
             Shi Liu},
    title =    {{Accurate force field of two-dimensional ferroelectrics from deep
             learning}},
    journal =  {Phys. Rev. B},
    year =     2021,
    volume =   104,
    issue =    17,
    pages =    174107,
    doi =      {10.1103/PhysRevB.104.174107},
}
@Article{Yang_CatalysisToday_2022_v387_p143,
    author =   {Manyi Yang and Luigi Bonati and Daniela Polino and Michele Parrinello},
    title =    {{Using metadynamics to build neural network potentials for reactive
             events: the case of urea decomposition in water}},
    journal =  {Catalysis Today},
    year =     2022,
    volume =   387,
    pages =    {143--149},
    doi =      {10.1016/j.cattod.2021.03.018},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S092058612100136X-ga1.jpg},
}
@Article{Zeng_NatCommun_2020_v11_p5713,
    author =   {Jinzhe Zeng and Liqun Cao and Mingyuan Xu and Tong Zhu and John Z H
             Zhang},
    title =    {{Complex reaction processes in combustion unraveled by neural network-
             based molecular dynamics simulation}},
    journal =  {Nat. Commun.},
    year =     2020,
    volume =   11,
    issue =    1,
    pages =    5713,
    annote =   {Combustion is a complex chemical system which involves thousands of
             chemical reactions and generates hundreds of molecular species and
             radicals during the process. In this work, a neural network-based
             molecular dynamics (MD) simulation is carried out to simulate the
             benchmark combustion of methane. During MD simulation, detailed
             reaction processes leading to the creation of specific molecular
             species including various intermediate radicals and the products are
             intimately revealed and characterized. Overall, a total of 798
             different chemical reactions were recorded and some new chemical
             reaction pathways were discovered. We believe that the present work
             heralds the dawn of a new era in which neural network-based reactive
             MD simulation can be practically applied to simulating important
             complex reaction systems at ab initio level, which provides atomic-
             level understanding of chemical reaction processes as well as
             discovery of new reaction pathways at an unprecedented level of detail
             beyond what laboratory experiments could accomplish.},
    PMCID =    {PMC7658983},
    doi =      {10.1038/s41467-020-19497-z},
}
@Article{Zhang_PhysRevB_2020_v102_p041121,
    author =   {Linfeng Zhang and Mohan Chen and Xifan Wu and Han Wang and Weinan E
             and Roberto Car},
    title =    {{Deep neural network for the dielectric response of insulators}},
    journal =  {Phys. Rev. B},
    year =     2020,
    volume =   102,
    issue =    4,
    pages =    041121,
    doi =      {10.1103/PhysRevB.102.041121},
}

@Article{Zeng_EnergyFuels_2021_v35_p762,
    author =   {Jinzhe Zeng and Linfeng Zhang and Han Wang and Tong Zhu},
    title =    {{Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep
             Potential GENerator}},
    journal =  {Energy Fuels},
    year =     2021,
    volume =   35,
    issue =    1,
    pages =    {762--769},
    doi =      {10.1021/acs.energyfuels.0c03211},
    image = {https://pubs.acs.org/cms/10.1021/acs.energyfuels.0c03211/asset/images/medium/ef0c03211_0006.gif},
}

@Article{Zeng_JChemTheoryComput_2021_v17_p6993,
    author =   {Jinzhe Zeng and Timothy J Giese and Şölen Ekesan and Darrin
             M York},
    title =    {{Development of Range-Corrected Deep Learning Potentials for Fast,
             Accurate Quantum Mechanical/Molecular Mechanical Simulations of
             Chemical Reactions in Solution}},
    journal =  {J. Chem. Theory Comput.},
    year =     2021,
    volume =   17,
    issue =    11,
    pages =    {6993--7009},
    annote =   {We develop a new deep potential─range correction (DPRc) machine
             learning potential for combined quantum mechanical/molecular
             mechanical (QM/MM) simulations of chemical reactions in the condensed
             phase. The new range correction enables short-ranged QM/MM
             interactions to be tuned for higher accuracy, and the correction
             smoothly vanishes within a specified cutoff. We further develop an
             active learning procedure for robust neural network training. We test
             the DPRc model and training procedure against a series of six
             nonenzymatic phosphoryl transfer reactions in solution that are
             important in mechanistic studies of RNA-cleaving enzymes.
             Specifically, we apply DPRc corrections to a base QM model and test
             its ability to reproduce free-energy profiles generated from a target
             QM model. We perform these comparisons using the MNDO/d and DFTB2
             semiempirical models because they differ in the way they treat orbital
             orthogonalization and electrostatics and produce free-energy profiles
             which differ significantly from each other, thereby providing us a
             rigorous stress test for the DPRc model and training procedure. The
             comparisons show that accurate reproduction of the free-energy
             profiles requires correction of the QM/MM interactions out to 6
             {\r{A}}. We further find that the model's initial training benefits
             from generating data from temperature replica exchange simulations and
             including high-temperature configurations into the fitting procedure,
             so the resulting models are trained to properly avoid high-energy
             regions. A single DPRc model was trained to reproduce four different
             reactions and yielded good agreement with the free-energy profiles
             made from the target QM/MM simulations. The DPRc model was further
             demonstrated to be transferable to 2D free-energy surfaces and 1D
             free-energy profiles that were not explicitly considered in the
             training. Examination of the computational performance of the DPRc
             model showed that it was fairly slow when run on CPUs but was sped up
             almost 100-fold when using NVIDIA V100 GPUs, resulting in almost
             negligible overhead. The new DPRc model and training procedure provide
             a potentially powerful new tool for the creation of next-generation
             QM/MM potentials for a wide spectrum of free-energy applications
             ranging from drug discovery to enzyme design.},
    PMCID =    {PMC8578402},
    doi =      {10.1021/acs.jctc.1c00201},
    image = {https://pubs.acs.org/cms/10.1021/acs.jctc.1c00201/asset/images/medium/ct1c00201_0008.gif},
}

@Article{Wang_ComputerPhysicsCommunications_2018_v228_p178,
    author =   {Han Wang and Linfeng Zhang and Jiequn Han and Weinan E},
    title =    {{DeePMD-kit: A deep learning package for many-body potential energy
             representation and molecular dynamics}},
    journal =  {Computer Physics Communications},
    year =     2018,
    volume =   228,
    pages =    {178--184},
    doi =      {10.1016/j.cpc.2018.03.016},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S0010465518300882-gr1.jpg},
}

@Article{Galib_Science_2021_v371_p921,
    author =   {Mirza Galib and David T Limmer},
    title =    {{Reactive uptake of N<sub>2</sub>O<sub>5</sub>
                          by atmospheric aerosol is dominated by
             interfacial processes}},
    journal =  {Science},
    year =     2021,
    volume =   371,
    issue =    6532,
    pages =    {921--925},
    annote =   {Nitrogen oxides are removed from the troposphere through the reactive
             uptake of N2O5 into aqueous aerosol. This process is thought to occur
             within the bulk of an aerosol, through solvation and subsequent
             hydrolysis. However, this perspective is difficult to reconcile with
             field measurements and cannot be verified directly because of the fast
             reaction kinetics of N2O5 Here, we use molecular simulations,
             including reactive potentials and importance sampling, to study the
             uptake of N2O5 into an aqueous aerosol. Rather than being mediated by
             the bulk, uptake is dominated by interfacial processes due to facile
             hydrolysis at the liquid-vapor interface and competitive
             reevaporation. With this molecular information, we propose an
             alternative interfacial reactive uptake model consistent with existing
             experimental observations.},
    doi =      {10.1126/science.abd7716},
    image = {https://www.science.org/cms/10.1126/science.abd7716/asset/8ee6d825-ba38-4334-b309-b6c6c586eff3/assets/graphic/371_921_f1.jpeg},
}

@Article{Miyagawa_ComputationalMaterialsScience_2022_v206_p111303,
    author =   {Takeru Miyagawa and Kazuki Mori and Nobuhiko Kato and Akio Yonezu},
    title =    {{Development of neural network potential for MD simulation and its
             application to TiN}},
    journal =  {Computational Materials Science},
    year =     2022,
    volume =   206,
    pages =    111303,
    doi =      {10.1016/j.commatsci.2022.111303},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S0927025622000970-ga1.jpg},
}

@Article{Cao_PhysChemChemPhys_2022,
    author =   {Liqun Cao and Jinzhe Zeng and Bo Wang and Tong Zhu and John Z.H. Zhang},
    title =    {{Ab Initio Neural Network MD Simulation of Thermal Decomposition of
             High Energy Material CL-20/TNT}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2022,
    annote =   {<jats:p>CL-20
             (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane, also
             known as HNIW) is one of the most powerful energetic materials.
             However, its high sensitivity to environmental stimuli greatly reduces
             its safety and severely limits its application....</jats:p>},
    doi =      {10.1039/D2CP00710J},
    volume = 24,
    pages = {11801--11811},
}

@Article{Lu_ComputerPhysicsCommunications_2021_v259_p107624,
    author =   {Denghui Lu and Han Wang and Mohan Chen and Lin Lin and Roberto Car and
             Weinan E and Weile Jia and Linfeng Zhang},
    title =    {{86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million
             atoms with ab initio accuracy}},
    journal =  {Computer Physics Communications},
    year =     2021,
    volume =   259,
    pages =    107624,
    doi =      {10.1016/j.cpc.2020.107624},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S001046552030299X-gr1.jpg},
}

@Article{Zhang_ComputerPhysicsCommunications_2020_v253_p107206,
    author =   {Yuzhi Zhang and Haidi Wang and Weijie Chen and Jinzhe Zeng and Linfeng
             Zhang and Han Wang and Weinan E},
    title =    {{DP-GEN: A concurrent learning platform for the generation of reliable
             deep learning based potential energy models}},
    journal =  {Computer Physics Communications},
    year =     2020,
    volume =   253,
    pages =    107206,
    doi =      {10.1016/j.cpc.2020.107206},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S001046552030045X-gr1.jpg},
}

@Article{Zhang_PhysRevMaterials_2019_v3_p023804,
    author =   {Linfeng Zhang and De-Ye Lin and Han Wang and Roberto Car and Weinan E},
    title =    {{Active learning of uniformly accurate interatomic potentials for
             materials simulation}},
    journal =  {Phys. Rev. Materials},
    year =     2019,
    volume =   3,
    issue =    2,
    pages =    023804,
    doi =      {10.1103/PhysRevMaterials.3.023804},
    image = {https://journals.aps.org/prmaterials/article/10.1103/PhysRevMaterials.3.023804/figures/1/medium},
}

@Article{Zhang_JChemPhys_2018_v149_p154107,
    author =   {Linfeng Zhang and Han Wang and Weinan E},
    title =    {{Adaptive coupling of a deep neural network potential to a classical
             force field}},
    journal =  {J. Chem. Phys.},
    year =     2018,
    volume =   149,
    issue =    15,
    pages =    154107,
    annote =   {An adaptive modeling method (AMM) that couples a deep neural network
             potential and a classical force field is introduced to address the
             accuracy-efficiency dilemma faced by the molecular simulation
             community. The AMM simulated system is decomposed into three types of
             regions. The first type captures the important phenomena in the system
             and requires high accuracy, for which we use the Deep Potential
             Molecular Dynamics (DeePMD) model in this work. The DeePMD model is
             trained to accurately reproduce the statistical properties of the ab
             initio molecular dynamics. The second type does not require high
             accuracy, and a classical force field is used to describe it in an
             efficient way. The third type is used for a smooth transition between
             the first and the second types of regions. By using a force
             interpolation scheme and imposing a thermodynamics force in the
             transition region, we make the DeePMD region embedded in the AMM
             simulated system as if it were embedded in a system that is fully
             described by the accurate potential. A representative example of the
             liquid water system is used to show the feasibility and promise of
             this method.},
    doi =      {10.1063/1.5042714},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/jcp/2018/jcp.2018.149.issue-15/1.5042714/20181017/images/small/1.5042714.figures.online.f1.gif},
}

@Article{Zhang_JChemPhys_2018_v149_p034101,
    author =   {Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E},
    title =    {{DeePCG: Constructing coarse-grained models via deep neural networks}},
    journal =  {J. Chem. Phys.},
    year =     2018,
    volume =   149,
    issue =    3,
    pages =    034101,
    annote =   {We introduce a general framework for constructing coarse-grained
             potential models without ad hoc approximations such as limiting the
             potential to two- and/or three-body contributions. The scheme, called
             the Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a
             carefully crafted neural network to construct a many-body coarse-
             grained potential. The network is trained with full atomistic data in
             a way that preserves the natural symmetries of the system. The
             resulting model is very accurate and can be used to sample the
             configurations of the coarse-grained variables in a much faster way
             than with the original atomistic model. As an application, we consider
             liquid water and use the oxygen coordinates as the coarse-grained
             variables, starting from a full atomistic simulation of this system at
             the ab initio molecular dynamics level. We find that the two-body,
             three-body, and higher-order oxygen correlation functions produced by
             the coarse-grained and full atomistic models agree very well with each
             other, illustrating the effectiveness of the DeePCG model on a rather
             challenging task.},
    doi =      {10.1063/1.5027645},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/jcp/2018/jcp.2018.149.issue-3/1.5027645/20180716/images/small/1.5027645.figures.online.f1.gif},
}

@Article{Ko_MolecularPhysics_2019_v117_p3269,
    author =   {Hsin-Yu Ko and Linfeng Zhang and Biswajit Santra and Han Wang and
             Weinan E and Robert A. {DiStasio Jr} and Roberto Car},
    title =    {{Isotope effects in liquid water via deep potential molecular dynamics}},
    journal =  {Molecular Physics},
    year =     2019,
    volume =   117,
    issue =    22,
    pages =    {3269--3281},
    doi =      {10.1080/00268976.2019.1652366},
    image = {https://www.tandfonline.com/na101/home/literatum/publisher/tandf/journals/content/tmph20/2019/tmph20.v117.i22/00268976.2019.1652366/20200220/images/medium/tmph_a_1652366_uf0001_oc.jpg},
}
@Article{Wang_ApplPhysLett_2019_v114_p244101,
    author =   {Hao Wang and Xun Guo and Linfeng Zhang and Han Wang and Jianming Xue},
    title =    {{Deep learning inter-atomic potential model for accurate irradiation
             damage simulations}},
    journal =  {Appl. Phys. Lett.},
    year =     2019,
    volume =   114,
    issue =    24,
    pages =    244101,
    doi =      {10.1063/1.5098061},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/apl/2019/apl.2019.114.issue-24/1.5098061/20190615/images/small/1.5098061.figures.online.f1.gif},
}
@Article{Xu_PhysRevB_2020_v102_p214113,
    author =   {Jianhang Xu and Chunyi Zhang and Linfeng Zhang and Mohan Chen and
             Biswajit Santra and Xifan Wu},
    title =    {{Isotope effects in molecular structures and electronic properties of
             liquid water via deep potential molecular dynamics based on the SCAN
             functional}},
    journal =  {Phys. Rev. B},
    year =     2020,
    volume =   102,
    issue =    21,
    pages =    214113,
    doi =      {10.1103/PhysRevB.102.214113},
}
@Article{Andreani_JPhysChemLett_2020_v11_p9461,
    author =   {Carla Andreani and Giovanni Romanelli and Alexandra Parmentier and
             Roberto Senesi and Alexander I Kolesnikov and Hsin-Yu Ko and Marcos F
             {Calegari Andrade} and Roberto Car},
    title =    {{Hydrogen Dynamics in Supercritical Water Probed by Neutron Scattering
             and Computer Simulations}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2020,
    volume =   11,
    issue =    21,
    pages =    {9461--9467},
    annote =   {In this work, an investigation of supercritical water is presented
             combining inelastic and deep inelastic neutron scattering experiments
             and molecular dynamics simulations based on a machine-learned
             potential of ab initio quality. The local hydrogen dynamics is
             investigated at 250 bar and in the temperature range of 553-823 K,
             covering the evolution from subcritical liquid to supercritical gas-
             like water. The evolution of libration, bending, and stretching
             motions in the vibrational density of states is studied, analyzing the
             spectral features by a mode decomposition. Moreover, the hydrogen
             nuclear momentum distribution is measured, and its anisotropy is
             probed experimentally. It is shown that hydrogen bonds survive up to
             the higher temperatures investigated, and we discuss our results in
             the framework of the coupling between intramolecular modes and
             intermolecular librations. Results show that the local potential
             affecting hydrogen becomes less anisotropic within the molecular plane
             in the supercritical phase, and we attribute this result to the
             presence of more distorted hydrogen bonds.},
    doi =      {10.1021/acs.jpclett.0c02547},
    image = {https://pubs.acs.org/cms/10.1021/acs.jpclett.0c02547/asset/images/medium/jz0c02547_0004.gif},
}

@Article{Li_MaterialsTodayPhysics_2020_v12_p100181,
    author =   {R. Li and E. Lee and T. Luo},
    title =    {{A unified deep neural network potential capable of predicting thermal
             conductivity of silicon in different phases}},
    journal =  {Materials Today Physics},
    year =     2020,
    volume =   12,
    pages =    100181,
    doi =      {10.1016/j.mtphys.2020.100181},
    image={https://ars.els-cdn.com/content/image/1-s2.0-S2542529320300055-fx1.jpg},
}

@Article{Wang_JPhysChemB_2020_v124_p3027,
    author =   {Zhilong Wang and Yanqiang Han and Jinjin Li and Xiao He},
    title =    {{Combining the Fragmentation Approach and Neural Network Potential
             Energy Surfaces of Fragments for Accurate Calculation of Protein
             Energy}},
    journal =  {J. Phys. Chem. B},
    year =     2020,
    volume =   124,
    issue =    15,
    pages =    {3027--3035},
    annote =   {Accurate and efficient all-atom quantum mechanical (QM) calculations
             for biomolecules still present a challenge to computational physicists
             and chemists. In this study, an extensible generalized molecular
             fractionation with a conjugate caps method combined with neural
             networks (NN-GMFCC) is developed for efficient QM calculation of
             protein energy. In the NN-GMFCC scheme, the total energy of a given
             protein is calculated by taking a proper combination of the high-
             precision neural network potential energies of all capped residues and
             overlapping conjugate caps. In addition, the two-body interaction
             energies of residue pairs are calculated by molecular mechanics (MM).
             With reference to the GMFCC/MM calculation at the
             {\ensuremath{\omega}}B97XD/6-31G* level, the overall mean unsigned
             errors of the energy deviations and atomic force root-mean-squared
             errors calculated by NN-GMFCC are only 2.01 kcal/mol and 0.68
             kcal/mol/{\r{A}}, respectively, for 14 proteins (containing up to
             13,728 atoms). Meanwhile, the NN-GMFCC approach is about 4 orders of
             magnitude faster than the GMFCC/MM method. The NN-GMFCC method could
             be systematically improved by inclusion of two-body QM interaction and
             multibody electronic polarization effect. Moreover, the NN-GMFCC
             approach can also be applied to other macromolecular systems such as
             DNA/RNA, and it is capable of providing a powerful and efficient
             approach for exploration of structures and functions of proteins with
             QM accuracy.},
    doi =      {10.1021/acs.jpcb.0c01370},
    image = {https://pubs.acs.org/cms/10.1021/acs.jpcb.0c01370/asset/images/medium/jp0c01370_0008.gif},
}
@Article{Xu_JPhysChemC_2020_v124_p16278,
    author =   {Nan Xu and Yao Shi and Yi He and Qing Shao},
    title =    {{A Deep-Learning Potential for Crystalline and Amorphous
             Li{-}Si Alloys}},
    journal =  {J. Phys. Chem. C},
    year =     2020,
    volume =   124,
    issue =    30,
    pages =    {16278--16288},
    doi =      {10.1021/acs.jpcc.0c03333},
    image = {https://pubs.acs.org/cms/10.1021/acs.jpcc.0c03333/asset/images/medium/jp0c03333_0010.gif},
}
@Article{Tang_PhysChemChemPhys_2020_v22_p18467,
    author =   {L {Tang                                  } and Z J {Yang
             } and T Q {Wen                                  } and K M {Ho
             } and M J {Kramer                                  } and C Z {Wang
             }},
    title =    {{Development of interatomic potential for Al{-}Tb alloys
             using a deep neural network learning method}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2020,
    volume =   22,
    issue =    33,
    pages =    {18467--18479},
    annote =   {An interatomic potential for the Al-Tb alloy around the composition of
             Al90Tb10 is developed using the deep neural network (DNN) learning
             method. The atomic configurations and the corresponding total
             potential energies and forces on each atom obtained from ab initio
             molecular dynamics (AIMD) simulations are collected to train a DNN
             model to construct the interatomic potential for the Al-Tb alloy. We
             show that the obtained DNN model can well reproduce the energies and
             forces calculated by AIMD simulations. Molecular dynamics (MD)
             simulations using the DNN interatomic potential also accurately
             describe the structural properties of the Al90Tb10 liquid, such as
             partial pair correlation functions (PPCFs) and bond angle
             distributions, in comparison with the results from AIMD simulations.
             Furthermore, the developed DNN interatomic potential predicts the
             formation energies of the crystalline phases of the Al-Tb system with
             an accuracy comparable to ab initio calculations. The structure
             factors of the Al90Tb10 metallic liquid and glass obtained by MD
             simulations using the developed DNN interatomic potential are also in
             good agreement with the experimental X-ray diffraction data. The
             development of short-range order (SRO) in the Al90Tb10 liquid and the
             undercooled liquid is also analyzed and three dominant SROs, i.e., Al-
             centered distorted icosahedron (DISICO) and Tb-centered '3661' and
             '15551' clusters, respectively, are identified.},
    doi =      {10.1039/D0CP01689F},
}
@Article{Zhang_PhysRevB_2020_v102_p115155,
    author =   {Chunyi Zhang and Linfeng Zhang and Jianhang Xu and Fujie Tang and
             Biswajit Santra and Xifan Wu},
    title =    {{Isotope effects in x-ray absorption spectra of liquid water}},
    journal =  {Phys. Rev. B},
    year =     2020,
    volume =   102,
    issue =    11,
    pages =    115155,
    doi =      {10.1103/PhysRevB.102.115155},
}
@Article{Zhang_PhysicsofPlasmas_2020_v27_p122704,
    author =   {Yuzhi Zhang and Chang Gao and Qianrui Liu and Linfeng Zhang and Han
             Wang and Mohan Chen},
    title =    {{Warm dense matter simulation via electron temperature dependent deep
             potential molecular dynamics}},
    journal =  {Physics of Plasmas},
    year =     2020,
    volume =   27,
    issue =    12,
    pages =    122704,
    doi =      {10.1063/5.0023265},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/php/2020/php.2020.27.issue-12/5.0023265/20201218/images/small/5.0023265.figures.online.f1.gif},
}
@Article{Liang_AdvTheorySimul_2020_v3_p2000180,
    author =   {Wenshuo Liang and Guimin Lu and Jianguo Yu},
    title =    {{Molecular Dynamics Simulations of Molten Magnesium Chloride Using
             Machine{-}Learning{-}Based Deep Potential}},
    journal =  {Adv. Theory Simul.},
    year =     2020,
    volume =   3,
    issue =    12,
    pages =    2000180,
    doi =      {10.1002/adts.202000180},
    image  = {https://onlinelibrary.wiley.com/cms/asset/a72ecdaf-9ee9-4a5e-a069-c4e0b28a8e02/adts202000180-fig-0001-m.png},
}
@Article{Pan_ComputationalMaterialsScience_2020_v185_p109955,
    author =   {Gechuanqi Pan and Pin Chen and Hui Yan and Yutong Lu},
    title =    {{A DFT accurate machine learning description of molten ZnCl2 and its
             mixtures: 1. Potential development and properties prediction of molten
             ZnCl2}},
    journal =  {Computational Materials Science},
    year =     2020,
    volume =   185,
    pages =    109955,
    doi =      {10.1016/j.commatsci.2020.109955},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S0927025620304468-ga1.jpg},
}
@Article{Dai_JournaloftheEuropeanCeramicSociety_2020_v40_p5029,
    author =   {Fu-Zhi Dai and Bo Wen and Huimin Xiang and Yanchun Zhou},
    title =    {{Grain boundary strengthening in ZrB2 by segregation of W: Atomistic
             simulations with deep learning potential}},
    journal =  {Journal of the European Ceramic Society},
    year =     2020,
    volume =   40,
    issue =    15,
    pages =    {5029--5036},
    doi =      {10.1016/j.jeurceramsoc.2020.06.007},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S0955221920304581-gr1.jpg},
}
@Article{Wang_FrontChem_2020_v8_p589795,
    author =   {Haidi Wang and Yuzhi Zhang and Linfeng Zhang and Han Wang},
    title =    {{Crystal Structure Prediction of Binary Alloys via Deep Potential}},
    journal =  {Front. Chem.},
    year =     2020,
    volume =   8,
    pages =    589795,
    annote =   {Predicting crystal structure has been a challenging problem in physics
             and materials science for a long time. A reliable energy calculation
             engine combined with an efficient global search algorithm, such as
             particle swarm optimization algorithm or genetic algorithm, is needed
             to conduct crystal structure prediction. In recent years, machine
             learning-based interatomic potential energy surface models have been
             proposed, potentially allowing us to perform crystal structure
             prediction for systems with the accuracy of density functional theory
             (DFT) and the speed of empirical force fields. In this paper, we
             employ a previously developed Deep Potential model to predict the
             intermetallic compound of the aluminum-magnesium system, and find six
             meta-stable phases with negative or nearly zero formation energy. In
             particular, Mg12Al8 shows excellent ductility and Mg5Al27 has a high
             Young's modulus. Based on our benchmark results, we propose a
             relatively robust structure screening criterion that selects
             potentially stable structures from the Deep Potential-based convex
             hull and performs DFT refinement. By using this criterion, the
             computational cost needed to construct the convex hull with ab initio
             accuracy can be dramatically reduced.},
    PMCID =    {PMC7732660},
    doi =      {10.3389/fchem.2020.589795},
    image = {https://www.frontiersin.org/files/Articles/589795/fchem-08-589795-HTML/image_t/fchem-08-589795-g001.gif},
}
@Article{Balyakin_PhysRevE_2020_v102_p052125,
    author =   {I A Balyakin and S V Rempel and R E Ryltsev and A A Rempel},
    title =    {{Deep machine learning interatomic potential for liquid silica}},
    journal =  {Phys. Rev. E},
    year =     2020,
    volume =   102,
    issue =    {5-1},
    pages =    052125,
    annote =   {The use of machine learning to develop neural network potentials (NNP)
             representing the interatomic potential energy surface allows us to
             achieve an optimal balance between accuracy and efficiency in computer
             simulation of materials. A key point in developing such potentials is
             the preparation of a training dataset of ab initio trajectories. Here
             we apply a deep potential molecular dynamics (DeePMD) approach to
             develop NNP for silica, which is the representative glassformer widely
             used as a model system for simulating network-forming liquids and
             glasses. We show that the use of a relatively small training dataset
             of high-temperature ab initio configurations is enough to fabricate
             NNP, which describes well both structural and dynamical properties of
             liquid silica. In particular, we calculate the pair correlation
             functions, angular distribution function, velocity autocorrelation
             functions, vibrational density of states, and mean-square displacement
             and reveal a close agreement with ab initio data. We show that NNP
             allows us to expand significantly the time-space scales achievable in
             simulations and thus calculating dynamical and transport properties
             with more accuracy than that for ab initio methods. We find that
             developed NNP allows us to describe the structure of the glassy silica
             with satisfactory accuracy even though no low-temperature
             configurations were included in the training procedure. The results
             obtained open up prospects for simulating structural and dynamical
             properties of liquids and glasses via NNP.},
    doi =      {10.1103/PhysRevE.102.052125},
}
@Article{CalegariAndrade_PhysRevMaterials_2020_v4_p113803,
    author =   {Marcos F. {Calegari Andrade} and Annabella Selloni},
    title =    {{Structure of disordered TiO2  phases from  <i>ab initio</i>  based deep neural network
             simulations}},
    journal =  {Phys. Rev. Materials},
    year =     2020,
    volume =   4,
    issue =    11,
    pages =    113803,
    doi =      {10.1103/PhysRevMaterials.4.113803},
}
@Article{Gartner_ProcNatlAcadSciUSA_2020_v117_p26040,
    author =   {Thomas E {Gartner 3rd} and Linfeng Zhang and Pablo M Piaggi and
             Roberto Car and Athanassios Z Panagiotopoulos and Pablo G Debenedetti},
    title =    {{Signatures of a liquid{-}liquid transition in an ab initio
             deep neural network model for water}},
    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
    year =     2020,
    volume =   117,
    issue =    42,
    pages =    {26040--26046},
    annote =   {The possible existence of a metastable liquid-liquid transition (LLT)
             and a corresponding liquid-liquid critical point (LLCP) in supercooled
             liquid water remains a topic of much debate. An LLT has been
             rigorously proved in three empirically parametrized molecular models
             of water, and evidence consistent with an LLT has been reported for
             several other such models. In contrast, experimental proof of this
             phenomenon has been elusive due to rapid ice nucleation under deeply
             supercooled conditions. In this work, we combined density functional
             theory (DFT), machine learning, and molecular simulations to shed
             additional light on the possible existence of an LLT in water. We
             trained a deep neural network (DNN) model to represent the ab initio
             potential energy surface of water from DFT calculations using the
             Strongly Constrained and Appropriately Normed (SCAN) functional. We
             then used advanced sampling simulations in the multithermal-multibaric
             ensemble to efficiently explore the thermophysical properties of the
             DNN model. The simulation results are consistent with the existence of
             an LLCP, although they do not constitute a rigorous proof thereof. We
             fit the simulation data to a two-state equation of state to provide an
             estimate of the LLCP's location. These combined results-obtained from
             a purely first-principles approach with no empirical parameters-are
             strongly suggestive of the existence of an LLT, bolstering the
             hypothesis that water can separate into two distinct liquid forms.},
    PMCID =    {PMC7584908},
    doi =      {10.1073/pnas.2015440117},
    image = {https://www.pnas.org/cms/10.1073/pnas.2015440117/asset/a4e91def-afd1-4efd-afbf-6ef13c2e014c/assets/images/large/pnas.2015440117fig01.jpg},
}
@Article{Wen_PhysRevB_2019_v100_p174101,
    author =   {Tongqi Wen and Cai-Zhuang Wang and M. J. Kramer and Yang Sun and
             Beilin Ye and Haidi Wang and Xueyuan Liu and Chao Zhang and Feng Zhang
             and Kai-Ming Ho and Nan Wang},
    title =    {{Development of a deep machine learning interatomic potential for
             metalloid-containing Pd-Si compounds}},
    journal =  {Phys. Rev. B},
    year =     2019,
    volume =   100,
    issue =    17,
    pages =    174101,
    doi =      {10.1103/PhysRevB.100.174101},
}
@article{Garcia_JPhysChemC_2021_v125_p27130,
    author = {Garcia, Juan C. and Gabriel, Joshua and Paulson, Noah H. and Low, John and Stan, Marius and Iddir, Hakim},
    title = {Insights from Computational Studies on the Anisotropic Volume Change of LixNiO2 at High States of Charge (x &lt; 0.25)},
    journal = {J. Phys. Chem. C},
    volume = {125},
    number = {49},
    pages = {27130-27139},
    year = {2021},
    doi = {10.1021/acs.jpcc.1c08022},
    image={https://pubs.acs.org/cms/10.1021/acs.jpcc.1c08022/asset/images/medium/jp1c08022_0009.gif},
}
@Article{Rodriguez_ACSApplMaterInterfaces_2021_v13_p55367,
    author =   {Alejandro Rodriguez and Stephen Lam and Ming Hu},
    title =    {{Thermodynamic and Transport Properties of LiF and FLiBe Molten Salts
             with Deep Learning Potentials}},
    journal =  {ACS Appl. Mater. Interfaces},
    year =     2021,
    volume =   13,
    issue =    46,
    pages =    {55367--55379},
    annote =   {Molten salts have attracted interest as potential heat carriers and/or
             fuel solvents in the development of new Gen IV nuclear reactor
             designs, high-temperature batteries, and thermal energy storage. In
             nuclear engineering, salts containing lithium fluoride-based compounds
             are of particular interest due to their ability to lower the melting
             points of mixtures and their compatibility with alloys. A machine
             learning potential (MLP) combined with a molecular dynamics study is
             performed on two popular molten salts, namely, LiF (50% Li) and FLiBe
             (66% LiF and 33% BeF2), to predict the thermodynamic and transport
             properties, such as density, diffusion coefficients, thermal
             conductivity, electrical conductivity, and shear viscosity. Due to the
             large possibilities of atomic environments, we employ training using
             Deep Potential Smooth Edition (DPSE) neural networks to learn from
             large datasets of 141,278 structures with 70 atoms for LiF and 238,610
             structures with 91 atoms for FLiBe molten salts. These networks are
             then deployed in fast molecular dynamics to predict the thermodynamic
             and transport properties that are only accessible at longer time
             scales and are otherwise difficult to calculate with classical
             potentials, ab initio molecular dynamics, or experiments. The prospect
             of this work is to provide guidance for future works to develop
             general MLPs for high-throughput thermophysical database generation
             for a wide spectrum of molten salts.},
    image = {https://pubs.acs.org/cms/10.1021/acsami.1c17942/asset/images/medium/am1c17942_0013.gif},
    doi = {10.1021/acsami.1c17942},
}
@Article{Tisi_PhysRevB_2021_v104_p224202,
    author =   {Davide Tisi and Linfeng Zhang and Riccardo Bertossa and Han Wang and
             Roberto Car and Stefano Baroni},
    title =    {{Heat transport in liquid water from first-principles and deep neural
             network simulations}},
    journal =  {Phys. Rev. B},
    year =     2021,
    volume =   104,
    issue =    22,
    pages =    224202,
    doi = {10.1103/PhysRevB.104.224202},
}

@Article{Wen_npjComputMater_2021_v7_p206,
    author =   {Tongqi Wen and Rui Wang and Lingyu Zhu and Linfeng Zhang and Han Wang
             and David J. Srolovitz and Zhaoxuan Wu},
    title =    {{Specialising neural network potentials for accurate properties and
             application to the mechanical response of titanium}},
    journal =  {npj Comput Mater},
    year =     2021,
    volume =   7,
    issue =    1,
    pages =    206,
    annote =   {<jats:title>Abstract</jats:title><jats:p>Large scale atomistic
             simulations provide direct access to important materials phenomena not
             easily accessible to experiments or quantum mechanics-based
             calculation approaches. Accurate and efficient interatomic potentials
             are the key enabler, but their development remains a challenge for
             complex materials and/or complex phenomena. Machine learning
             potentials, such as the Deep Potential (DP) approach, provide robust
             means to produce general purpose interatomic potentials. Here, we
             provide a methodology for specialising machine learning potentials for
             high fidelity simulations of complex phenomena, where general
             potentials do not suffice. As an example, we specialise a general
             purpose DP method to describe the mechanical response of two
             allotropes of titanium (in addition to other defect, thermodynamic and
             structural properties). The resulting DP correctly captures the
             structures, energies, elastic constants and
             <jats:italic>{\ensuremath{\gamma}}</jats:italic>-lines of Ti in both
             the HCP and BCC structures, as well as properties such as dislocation
             core structures, vacancy formation energies, phase transition
             temperatures, and thermal expansion. The DP thus enables direct
             atomistic modelling of plastic and fracture behaviour of Ti. The
             approach to specialising DP interatomic potential, DPspecX, for
             accurate reproduction of properties of interest
             {\textquotedblleft}X{\textquotedblright}, is general and extensible to
             other systems and properties.</jats:p>},
    doi = {10.1038/s41524-021-00661-y},
}
@Article{Gupta_EnergyEnvironSci_2021_v14_p6554,
author ="Gupta, Mayanak K. and Ding, Jingxuan and Osti, Naresh C. and Abernathy, Douglas L. and Arnold, William and Wang, Hui and Hood, Zachary and Delaire, Olivier",
title  ="Fast Na diffusion and anharmonic phonon dynamics in superionic Na3PS4",
journal  ="Energy Environ. Sci.",
year  ="2021",
volume  ="14",
issue  ="12",
pages  ="6554-6563",
publisher  ="The Royal Society of Chemistry",
doi  ="10.1039/D1EE01509E",
url  ="http://dx.doi.org/10.1039/D1EE01509E",
abstract  ="The design of new solid electrolytes (SEs) hinges on identifying and tuning relevant descriptors. Phonons describe the atomic dynamics in crystalline materials and provide a basis to encode possible minimum energy pathways for ion migration{,} but anharmonic effects can be large in SEs. Identifying and controlling the pertinent phonon modes coupled most strongly with ionic conductivity{,} and assessing the role of anharmonicity{,} could therefore pave the way for discovering and designing new SEs via phonon engineering. Here{,} we investigate phonons in Na3PS4 and their coupling to fast Na diffusion{,} using a combination of neutron scattering{,} ab initio molecular dynamics (AIMD){,} and extended molecular dynamics based on machine-learned potentials. We identify that anharmonic soft-modes at the Brillouin zone boundary of the anharmonically stabilized cubic phase constitute key phonon modes that control the Na diffusion process in Na3PS4. We demonstrate how these strongly anharmonic phonon modes enable Na-ions to hop along the minimum energy pathways. Further{,} the quasi-elastic neutron scattering (QENS) measurements{,} supplemented with ab initio and machine-learned molecular dynamics simulations{,} probe the Na diffusivity and diffusion mechanism. These results offer detailed microscopic insights into the dynamic mechanism of fast Na diffusion and provide an avenue to search for further Na solid electrolytes."
}

@Article{Cheng_NatCommun_2021_v12_p6901,
    author =   {Zhe Cheng and Ruiyang Li and Xingxu Yan and Glenn Jernigan and
             Jingjing Shi and Michael E Liao and Nicholas J Hines and Chaitanya A
             Gadre and Juan Carlos Idrobo and Eungkyu Lee and Karl D Hobart and
             Mark S Goorsky and Xiaoqing Pan and Tengfei Luo and Samuel Graham},
    title =    {{Experimental observation of localized interfacial phonon modes}},
    journal =  {Nat. Commun.},
    year =     2021,
    volume =   12,
    issue =    1,
    pages =    6901,
    annote =   {Interfaces impede heat flow in micro/nanostructured systems.
             Conventional theories for interfacial thermal transport were derived
             based on bulk phonon properties of the materials making up the
             interface without explicitly considering the atomistic interfacial
             details, which are found critical to correctly describing thermal
             boundary conductance. Recent theoretical studies predicted the
             existence of localized phonon modes at the interface which can play an
             important role in understanding interfacial thermal transport.
             However, experimental validation is still lacking. Through a
             combination of Raman spectroscopy and high-energy-resolution electron
             energy-loss spectroscopy in a scanning transmission electron
             microscope, we report the experimental observation of localized
             interfacial phonon modes at ~12{\,}THz at a high-quality epitaxial Si-
             Ge interface. These modes are further confirmed using molecular
             dynamics simulations with a high-fidelity neural network interatomic
             potential, which also yield thermal boundary conductance agreeing well
             with that measured in time-domain thermoreflectance experiments.
             Simulations find that the interfacial phonon modes have an obvious
             contribution to the total thermal boundary conductance. Our findings
             significantly contribute to the understanding of interfacial thermal
             transport physics and have impact on engineering thermal boundary
             conductance at interfaces in applications such as electronics thermal
             management and thermoelectric energy conversion.},
    PMCID =    {PMC8617064},
    doi = {10.1038/s41467-021-27250-3},
}

@Article{Shi_MaterialsScienceinSemiconductorProcessing_2021_v136_p106146,
    author =   {Mengchao Shi and Junhua Li and Ming Tao and Xin Zhang and Jie Liu},
    title =    {{Artificial intelligence model for efficient simulation of monatomic
             phase change material antimony}},
    journal =  {Materials Science in Semiconductor Processing},
    year =     2021,
    volume =   136,
    pages =    106146,
    doi =      {10.1016/j.mssp.2021.106146},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S1369800121004868-gr1.jpg},
}

@Article{Tang_JChemPhys_2021_v155_p194503,
    author =   {L Tang and K M Ho and C Z Wang},
    title =    {{Molecular dynamics simulation of metallic Al{-}Ce liquids
             using a neural network machine learning interatomic potential}},
    journal =  {J. Chem. Phys.},
    year =     2021,
    volume =   155,
    issue =    19,
    pages =    194503,
    annote =   {Al-rich Al-Ce alloys have the possibility of replacing heavier steel
             and cast irons for use in high-temperature applications. Knowledge
             about the structures and properties of Al-Ce alloys at the liquid
             state is vital for optimizing the manufacture process to produce
             desired alloys. However, reliable molecular dynamics simulation of Al-
             Ce alloy systems remains a great challenge due to the lack of accurate
             Al-Ce interatomic potential. Here, an artificial neural network (ANN)
             deep machine learning (ML) method is used to develop a reliable
             interatomic potential for Al-Ce alloys. Ab initio molecular dynamics
             simulation data on the Al-Ce liquid with a small unit cell
             ({\ensuremath{\sim}}200 atoms) and on the known Al-Ce crystalline
             compounds are collected to train the interatomic potential using ANN-
             ML. The obtained ANN-ML model reproduces well the energies, forces,
             and atomic structure of the Al90Ce10 liquid and crystalline phases of
             Al-Ce compounds in comparison with the ab initio results. The
             developed ANN-ML potential is applied in molecular dynamics
             simulations to study the structures and properties of the metallic
             Al90Ce10 liquid, which would provide useful insight into the guiding
             experimental process to produce desired Al-Ce alloys.},
    doi =      {10.1063/5.0066061},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/jcp/2021/jcp.2021.155.issue-19/5.0066061/20211115/images/small/5.0066061.figures.online.f1.gif},
}

@Article{Pinheiro_ChemSci_2021_v12_p14396,
    author =   {Max {Pinheiro Jr} and Fuchun Ge and Nicolas Ferr{\'e} and Pavlo O Dral
             and Mario Barbatti},
    title =    {{Choosing the right molecular machine learning potential}},
    journal =  {Chem. Sci.},
    year =     2021,
    volume =   12,
    issue =    43,
    pages =    {14396--14413},
    annote =   {Quantum-chemistry simulations based on potential energy surfaces of
             molecules provide invaluable insight into the physicochemical
             processes at the atomistic level and yield such important observables
             as reaction rates and spectra. Machine learning potentials promise to
             significantly reduce the computational cost and hence enable otherwise
             unfeasible simulations. However, the surging number of such potentials
             begs the question of which one to choose or whether we still need to
             develop yet another one. Here, we address this question by evaluating
             the performance of popular machine learning potentials in terms of
             accuracy and computational cost. In addition, we deliver structured
             information for non-specialists in machine learning to guide them
             through the maze of acronyms, recognize each potential's main
             features, and judge what they could expect from each one.},
    PMCID =    {PMC8580106},
    doi =      {10.1039/d1sc03564a},
}

@Article{Wang_JournalofAppliedPhysics_2021_v130_p185103,
    author =   {Q. Wang and B. Zhai and H. P. Wang and B. Wei},
    title =    {{Atomic structure of liquid refractory Nb<sub>5</sub>Si<sub>3</sub>
             intermetallic compound alloy based upon deep neural network potential}},
    journal =  {Journal of Applied Physics},
    year =     2021,
    volume =   130,
    issue =    18,
    pages =    185103,
    doi =      {10.1063/5.0067157},
    image = {https://aip.scitation.org/na101/home/literatum/publisher/aip/journals/content/jap/2021/jap.2021.130.issue-18/5.0067157/20211108/images/small/5.0067157.figures.online.f1.gif},
}

@article{Daniels_JPhysChemC_2021_v125_p24440,
    author = {Daniels, Carena L. and Liu, Da-Jiang and Adamson, Marquix A. S. and Knobeloch, Megan and Vela, Javier},
    title = {Azo(xy) vs Aniline Selectivity in Catalytic Nitroarene Reduction by Intermetallics: Experiments and Simulations},
    journal = {J. Phys. Chem. C},
    volume = {125},
    number = {44},
    pages = {24440-24450},
    year = {2021},
    doi = {10.1021/acs.jpcc.1c08569},
    image = {https://pubs.acs.org/cms/10.1021/acs.jpcc.1c08569/asset/images/medium/jp1c08569_0012.gif},
}

@Article{Calio_JAmChemSoc_2021_v143_p18672,
    author =   {Paul B Calio and Chenghan Li and Gregory A Voth},
    title =    {{Resolving the Structural Debate for the Hydrated Excess Proton in
             Water}},
    journal =  {J. Am. Chem. Soc.},
    year =     2021,
    volume =   143,
    issue =    44,
    pages =    {18672--18683},
    annote =   {It has long been proposed that the hydrated excess proton in water
             (aka the solvated "hydronium" cation) likely has two limiting forms,
             that of the Eigen cation (H9O4+) and that of the Zundel cation
             (H5O2+). There has been debate over which of these two is the more
             dominant species and/or whether intermediate (or "distorted")
             structures between these two limits are the more realistic
             representation. Spectroscopy experiments have recently provided
             further results regarding the excess proton. These experiments show
             that the hydrated proton has an anisotropy reorientation time scale on
             the order of 1-2 ps. This time scale has been suggested to possibly
             contradict the picture of the more rapid "special pair dance"
             phenomenon for the hydrated excess proton, which is a signature of a
             distorted Eigen cation. The special pair dance was predicted from
             prior computational studies in which the hydrated central core
             hydronium structure continually switches
             (O-H{\textperiodcentered}{\textperiodcentered}{\textperiodcentered}O)*
             special pair hydrogen-bond partners with the closest three water
             molecules, yielding on average a distorted Eigen cation with three
             equivalent and dynamically exchanging distortions. Through state-of-
             art simulations it is shown here that anisotropy reorientation time
             scales of the same magnitude are obtained that also include structural
             reorientations associated with the special pair dance, leading to a
             reinterpretation of the experimental results. These results and
             additional analyses point to a distorted and dynamic Eigen cation as
             the most prevalent hydrated proton species in aqueous acid solutions
             of dilute to moderate concentration, as opposed to a stabilized or a
             distorted (but not "dancing") Zundel cation.},
    doi =      {10.1021/jacs.1c08552},
    image = {https://pubs.acs.org/cms/10.1021/jacs.1c08552/asset/images/medium/ja1c08552_0005.gif},
}

@Article{Cheng_AIPAdvances_2021_v11_p015043,
    author =   {Yuqing Cheng and Han Wang and Shuaichuang Wang and Xingyu Gao and
             Qiong Li and Jun Fang and Hongzhou Song and Weidong Chu and Gongmu
             Zhang and Haifeng Song and Haifeng Liu},
    title =    {{Deep-learning potential method to simulate shear viscosity of liquid
             aluminum at high temperature and high pressure by molecular dynamics}},
    journal =  {AIP Advances},
    year =     2021,
    volume =   11,
    issue =    1,
    pages =    015043,
    doi =      {10.1063/5.0036298},
}

@Article{Chen_ChinJChem_2021_v39_p3029,
    author =   {Dingming Chen and Zhuangzhuang Lai and Jiawei Zhang and Jianfu Chen
             and Peijun Hu and Haifeng Wang},
    title =    {{Gold Segregation Improves Electrocatalytic Activity of Icosahedron
             Au@Pt Nanocluster: Insights from Machine Learning
             }},
    journal =  {Chin. J. Chem.},
    year =     2021,
    volume =   39,
    issue =    11,
    pages =    {3029--3036},
    doi =      {10.1002/cjoc.202100352},
}

@Article{Shi_JPhysChemLett_2021_v12_p10310,
    author =   {Yu Shi and Carrie C Doyle and Thomas L Beck},
    title =    {{Condensed Phase Water Molecular Multipole Moments from Deep Neural
             Network Models Trained on <i>Ab Initio</i> Simulation Data}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2021,
    volume =   12,
    issue =    42,
    pages =    {10310--10317},
    annote =   {Ionic solvation phenomena in liquids involve intense interactions in
             the inner solvation shell. For interactions beyond the first shell,
             the ion-solvent interaction energies result from the sum of many
             smaller-magnitude contributions that can still include polarization
             effects. Deep neural network (DNN) methods have recently found wide
             application in developing efficient molecular models that maintain
             near-quantum accuracy. Here we extend the DeePMD-kit code to produce
             accurate molecular multipole moments in the bulk and near interfaces.
             The new method is validated by comparing the DNN moments with those
             generated by ab initio simulations. The moments are used to compute
             the electrostatic potential at the center of a molecular-sized
             hydrophobic cavity in water. The results show that the fields produced
             by the DNN models are in quantitative agreement with the AIMD-derived
             values. These efficient methods will open the door to more accurate
             solvation models for large solutes such as proteins.},
    doi =      {10.1021/acs.jpclett.1c02328},
}

@Article{Niblett_JChemPhys_2021_v155_p164101,
    author =   {Samuel P Niblett and Mirza Galib and David T Limmer},
    title =    {{Learning intermolecular forces at liquid{-}vapor interfaces}},
    journal =  {J. Chem. Phys.},
    year =     2021,
    volume =   155,
    issue =    16,
    pages =    164101,
    annote =   {By adopting a perspective informed by contemporary liquid-state
             theory, we consider how to train an artificial neural network
             potential to describe inhomogeneous, disordered systems. We find that
             neural network potentials based on local representations of atomic
             environments are capable of describing some properties of liquid-vapor
             interfaces but typically fail for properties that depend on unbalanced
             long-ranged interactions that build up in the presence of broken
             translation symmetry. These same interactions cancel in the
             translationally invariant bulk, allowing local neural network
             potentials to describe bulk properties correctly. By incorporating
             explicit models of the slowly varying long-ranged interactions and
             training neural networks only on the short-ranged components, we can
             arrive at potentials that robustly recover interfacial properties. We
             find that local neural network models can sometimes approximate a
             local molecular field potential to correct for the truncated
             interactions, but this behavior is variable and hard to learn.
             Generally, we find that models with explicit electrostatics are easier
             to train and have higher accuracy. We demonstrate this perspective in
             a simple model of an asymmetric dipolar fluid, where the exact long-
             ranged interaction is known, and in an ab initio water model, where it
             is approximated.},
    doi =      {10.1063/5.0067565},
}

@Article{Zhang_JPhysChemB_2021_v125_p11444,
    author =   {Chunyi Zhang and Fujie Tang and Mohan Chen and Jianhang Xu and Linfeng
             Zhang and Diana Y Qiu and John P Perdew and Michael L Klein and Xifan
             Wu},
    title =    {{Modeling Liquid Water by Climbing up Jacob{\textquoteright}s Ladder in
             Density Functional Theory Facilitated by Using Deep Neural Network
             Potentials}},
    journal =  {J. Phys. Chem. B},
    year =     2021,
    volume =   125,
    issue =    41,
    pages =    {11444--11456},
    annote =   {Within the framework of Kohn-Sham density functional theory (DFT), the
             ability to provide good predictions of water properties by employing a
             strongly constrained and appropriately normed (SCAN) functional has
             been extensively demonstrated in recent years. Here, we further
             advance the modeling of water by building a more accurate model on the
             fourth rung of Jacob's ladder with the hybrid functional, SCAN0. In
             particular, we carry out both classical and Feynman path-integral
             molecular dynamics calculations of water with the SCAN0 functional and
             the isobaric-isothermal ensemble. To generate the equilibrated
             structure of water, a deep neural network potential is trained from
             the atomic potential energy surface based on ab initio data obtained
             from SCAN0 DFT calculations. For the electronic properties of water, a
             separate deep neural network potential is trained by using the Deep
             Wannier method based on the maximally localized Wannier functions of
             the equilibrated trajectory at the SCAN0 level. The structural,
             dynamic, and electric properties of water were analyzed. The hydrogen-
             bond structures, density, infrared spectra, diffusion coefficients,
             and dielectric constants of water, in the electronic ground state, are
             computed by using a large simulation box and long simulation time. For
             the properties involving electronic excitations, we apply the GW
             approximation within many-body perturbation theory to calculate the
             quasiparticle density of states and bandgap of water. Compared to the
             SCAN functional, mixing exact exchange mitigates the self-interaction
             error in the meta-generalized-gradient approximation and further
             softens liquid water toward the experimental direction. For most of
             the water properties, the SCAN0 functional shows a systematic
             improvement over the SCAN functional. However, some important
             discrepancies remain. The H-bond network predicted by the SCAN0
             functional is still slightly overstructured compared to the
             experimental results.},
    doi =      {10.1021/acs.jpcb.1c03884},
}

@Article{ZepedaNunez_JournalofComputationalPhysics_2021_v443_p110523,
    author =   {Leonardo Zepeda-N{\'u}{\~n}ez and Yixiao Chen and Jiefu Zhang and
             Weile Jia and Linfeng Zhang and Lin Lin},
    title =    {{Deep Density: Circumventing the Kohn-Sham equations via symmetry
             preserving neural networks}},
    journal =  {Journal of Computational Physics},
    year =     2021,
    volume =   443,
    pages =    110523,
    doi =      {10.1016/j.jcp.2021.110523},
}

@Article{Jin_PhysChemChemPhys_2021_v23_p21470,
    author =   {Lujie Jin and Yujin Ji and Hongshuai Wang and Lifeng Ding and Youyong
             Li},
    title =    {{First-principles materials simulation and design for alkali and
             alkaline metal ion batteries accelerated by machine learning}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2021,
    volume =   23,
    issue =    38,
    pages =    {21470--21483},
    annote =   {The challenge of regeneration of batteries requires a performance
             improvement in the alkali/alkaline metal ion battery (AMIB) materials,
             whereas the traditional research paradigm fully based on experiments
             and theoretical simulations needs massive research and development
             investment. During the last decade, machine learning (ML) has made
             breakthroughs in many complex disciplines, which testifies to their
             high processing speed and ability to capture relationships. Inspired
             by these achievements, ML has also been introduced to bring a new
             paradigm for shortening the development of AMIB materials. In this
             Perspective, the focus will be on how this new ML technology solves
             the key problems of redox potentials, ionic conductivity and stability
             parameters in first-principles materials' simulation and design for
             AMIBs. It is found that ML not only accelerates the property
             prediction, but also gives physicochemical insights into AMIB
             materials' design. In addition, the final part of this paper
             summarizes current achievements and looks forward to the progress of a
             novel paradigm in direct/inverse design with the increasing number of
             databases, skills, and ML technologies for AMIBs.},
    doi =      {10.1039/d1cp02963k},
}

@Article{Bu_SolarEnergyMaterialsandSolarCells_2021_v232_p111346,
    author =   {Min Bu and Wenshuo Liang and Guimin Lu and Jianguo Yu},
    title =    {{Local structure elucidation and properties prediction on
             KCl{-}CaCl2 molten salt: A deep potential molecular dynamics
             study}},
    journal =  {Solar Energy Materials and Solar Cells},
    year =     2021,
    volume =   232,
    pages =    111346,
    doi =      {10.1016/j.solmat.2021.111346},
}

@Article{Torres_JPhysChemB_2021_v125_p10772,
    author =   {Alberto Torres and Luana S Pedroza and Marivi Fernandez-Serra and
             Alexandre R Rocha},
    title =    {{Using Neural Network Force Fields to Ascertain the Quality of <i>Ab
             Initio</i> Simulations of Liquid Water}},
    journal =  {J. Phys. Chem. B},
    year =     2021,
    volume =   125,
    issue =    38,
    pages =    {10772--10778},
    annote =   {Accurately simulating the properties of bulk water, despite the
             apparent simplicity of the molecule, is still a challenge. In order to
             fully understand and reproduce its complex phase diagram, it is
             necessary to perform simulations at the ab initio level, including
             quantum mechanical effects both for electrons and nuclei. This comes
             at a high computational cost, given that the structural and dynamical
             properties tend to require long timescales and large simulation cells.
             In this work, we evaluate the errors that density functional theory
             (DFT)-based simulations routinely incur into due time- and size-scale
             limitations. These errors are evaluated using neural-network-trained
             force fields that are accurate at the level of DFT methods. We compare
             different exchange and correlation potentials for properties of bulk
             water that require large timescales. We show that structural
             properties are less dependent on the system size and that dynamical
             properties such as the diffusion coefficient have a strong dependence
             on the simulation size and timescale. Our results facilitate
             comparisons of DFT-based simulation results with experiments and offer
             a path to discriminate between model and convergence errors in these
             simulations.},
    doi =      {10.1021/acs.jpcb.1c04372},
}

@Article{Deng_GeophysResLett_2021_v48_p093806,
    author =   {Jie Deng and Lars Stixrude},
    title =    {{Thermal Conductivity of Silicate Liquid Determined by Machine Learning
             Potentials}},
    journal =  {Geophys Res Lett},
    year =     2021,
    volume =   48,
    issue =    17,
    pages = {e2021GL093806},
    doi =      {10.1029/2021GL093806},
}

@Article{Zeng_PhysRevResearch_2021_v3_p033116,
    author =   {Qiyu Zeng and Xiaoxiang Yu and Yunpeng Yao and Tianyu Gao and Bo Chen
             and Shen Zhang and Dongdong Kang and Han Wang and Jiayu Dai},
    title =    {{<i>Ab initio</i>  validation on the connection between atomistic and
             hydrodynamic description to unravel the ion dynamics of warm dense
             matter}},
    journal =  {Phys. Rev. Research},
    year =     2021,
    volume =   3,
    issue =    3,
    pages =    033116,
    doi =      {10.1103/PhysRevResearch.3.033116},
}

@Article{Yang_PhysRevLett_2021_v127_p080603,
    author =   {Manyi Yang and Tarak Karmakar and Michele Parrinello},
    title =    {{Liquid-Liquid Critical Point in Phosphorus}},
    journal =  {Phys. Rev. Lett.},
    year =     2021,
    volume =   127,
    issue =    8,
    pages =    080603,
    annote =   {The study of liquid-liquid phase transitions has attracted
             considerable attention. One interesting example of this phenomenon is
             phosphorus, for which the existence of a first-order phase transition
             between a low density insulating molecular phase and a conducting
             polymeric phase has been experimentally established. In this Letter,
             we model this transition by an ab{~}initio quality molecular dynamics
             simulation and explore a large portion of the liquid section of the
             phase diagram. We draw the liquid-liquid coexistence curve and
             determine that it terminates into a second-order critical point. Close
             to the critical point, large coupled structure and electronic
             structure fluctuations are observed.},
    doi =      {10.1103/PhysRevLett.127.080603},
}


@article{Andolina_JPhysChemC_2021_v125_p17438,
    author = {Andolina, Christopher M. and Bon, Marta and Passerone, Daniele and Saidi, Wissam A.},
    title = {Robust, Multi-Length-Scale, Machine Learning Potential for Ag–Au Bimetallic Alloys from Clusters to Bulk Materials},
    journal = {J. Phys. Chem. C},
    volume = {125},
    number = {31},
    pages = {17438-17447},
    year = {2021},
    doi = {10.1021/acs.jpcc.1c04403},
}
@Article{Akhanda_PhysRevMater_2021_v5_p083804,
    author =   {Md Sabbir Akhanda and S Emad Rezaei and Keivan Esfarjani and Sergiy
             Krylyuk and Albert V Davydov and Mona Zebarjadi},
    title =    {{Improved Al-Mg alloy surface segregation predictions with a machine
             learning atomistic potential}},
    journal =  {Phys. Rev. Mater.},
    year =     2021,
    volume =   5,
    issue =    1,
    pages =    083804,
    annote =   {Magneto-thermoelectric transport provides an understanding of coupled
             electron-hole-phonon current in topological materials and has
             applications in energy conversion and cooling. In this work, we study
             the Nernst coefficient, the magneto-Seebeck coefficient, and the
             magnetoresistance of single-crystalline Bi2Te3 under external magnetic
             field in the range of -3 T to 3 T and in the temperature range of 55 K
             to 380 K. Moreau's relation is employed to justify both the overall
             trend of the Nernst coefficient and the temperature at which the
             Nernst coefficient changes sign. We observe a non-linear relationship
             between the Nernst coefficient and the applied magnetic field in the
             temperature range of 55 K to 255 K. An increase in both the Nernst
             coefficient and the magneto-Seebeck coefficient is observed as the
             temperature is reduced which can be attributed to the increased
             mobility of the carriers at lower temperatures. First-principles
             density functional theory calculations were carried out to physically
             model the experimental data including electronic and transport
             properties. Simulation findings agreed with the experiments and
             provide a theoretical insight to justify the measurements.},
    PMCID =    {PMC8268759},
    doi =      {10.1103/PhysRevMaterials.5.083804},
}

@Article{Luo_GeophysResLett_2021_v48_p093573,
    author =   {Haiyang Luo and Bijaya B. Karki and Dipta B. Ghosh and Huiming Bao},
    title =    {{Anomalous Behavior of Viscosity and Electrical Conductivity of MgSiO
             <sub>3</sub>             Melt at Mantle Conditions}},
    journal =  {Geophys Res Lett},
    year =     2021,
    volume =   48,
    issue =    13,
    doi =      {10.1029/2021GL093573},
    page = {e2021GL093573},
}

@Article{Xu_FrontChem_2021_v9_p692200,
    author =   {Mingyuan Xu and Tong Zhu and John Z H Zhang},
    title =    {{Automatically Constructed Neural Network Potentials for Molecular
             Dynamics Simulation of Zinc Proteins}},
    journal =  {Front. Chem.},
    year =     2021,
    volume =   9,
    pages =    692200,
    annote =   {The development of accurate and efficient potential energy functions
             for the molecular dynamics simulation of metalloproteins has long been
             a great challenge for the theoretical chemistry community. An
             artificial neural network provides the possibility to develop
             potential energy functions with both the efficiency of the classical
             force fields and the accuracy of the quantum chemical methods. In this
             work, neural network potentials were automatically constructed by
             using the ESOINN-DP method for typical zinc proteins. For the four
             most common zinc coordination modes in proteins, the potential energy,
             atomic forces, and atomic charges predicted by neural network models
             show great agreement with quantum mechanics calculations and the
             neural network potential can maintain the coordination geometry
             correctly. In addition, MD simulation and energy optimization with the
             neural network potential can be readily used for structural
             refinement. The neural network potential is not limited by the
             function form and complex parameterization process, and important
             quantum effects such as polarization and charge transfer can be
             accurately considered. The algorithm proposed in this work can also be
             directly applied to proteins containing other metal ions.},
    PMCID =    {PMC8249736},
    doi =      {10.3389/fchem.2021.692200},
}

@Article{Luo_GeochimicaetCosmochimicaActa_2021_v303_p38,
    author =   {Haiyang Luo and Bijaya B. Karki and Dipta B. Ghosh and Huiming Bao},
    title =    {{Deep neural network potentials for diffusional lithium isotope
             fractionation in silicate melts}},
    journal =  {Geochimica et Cosmochimica Acta},
    year =     2021,
    volume =   303,
    pages =    {38--50},
    doi =      {10.1016/j.gca.2021.03.031},
}

@Article{Zhang_PhysRevLett_2021_v126_p236001,
    author =   {Linfeng Zhang and Han Wang and Roberto Car and Weinan E},
    title =    {{Phase Diagram of a Deep Potential Water Model}},
    journal =  {Phys. Rev. Lett.},
    year =     2021,
    volume =   126,
    issue =    23,
    pages =    236001,
    annote =   {Using the Deep Potential methodology, we construct a model that
             reproduces accurately the potential energy surface of the SCAN
             approximation of density functional theory for water, from low
             temperature and pressure to about 2400{~}K and 50{~}GPa, excluding the
             vapor stability region. The computational efficiency of the model
             makes it possible to predict its phase diagram using molecular
             dynamics. Satisfactory overall agreement with experimental results is
             obtained. The fluid phases, molecular and ionic, and all the stable
             ice polymorphs, ordered and disordered, are predicted correctly, with
             the exception of ice III and XV that are stable in experiments, but
             metastable in the model. The evolution of the atomic dynamics upon
             heating, as ice VII transforms first into ice VII^{''} and then into
             an ionic fluid, reveals that molecular dissociation and breaking of
             the ice rules coexist with strong covalent fluctuations, explaining
             why only partial ionization was inferred in experiments.},
    doi =      {10.1103/PhysRevLett.126.236001},
}

@article{Liang_JMaterSciTech_2021_v75_p78,
    title = {Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials},
    journal = {Journal of Materials Science & Technology},
    volume = {75},
    pages = {78-85},
    year = {2021},
    issn = {1005-0302},
    doi = {10.1016/j.jmst.2020.09.040},
    url = {https://www.sciencedirect.com/science/article/pii/S1005030220309075},
    author = {Wenshuo Liang and Guimin Lu and Jianguo Yu},
    keywords = {Deep potentials, Molecular dynamics simulations, Alkali chlorides, Local structure, Transport properties},
    abstract = {In this work, the local structure and transport properties of three typical alkali chlorides (LiCl, NaCl, and KCl) were investigated by our newly trained deep potentials (DPs). We extracted datasets from ab initio molecular dynamics (AIMD) calculations and used these to train and validate the DPs. Large-scale and long-time molecular dynamics simulations were performed over a wider range of temperatures than AIMD to confirm the reliability and generality of the DPs. We demonstrated that the generated DPs can serve as a powerful tool for simulating alkali chlorides; the DPs also provide results with accuracy that is comparable to that of AIMD and efficiency that is similar to that of empirical potentials. The partial radial distribution functions and angle distribution functions predicted using the DPs are in close agreement with those derived from AIMD. The estimated densities, self-diffusion coefficients, shear viscosities, and electrical conductivities also matched well with the AIMD and experimental data. This work provides confidence that DPs can be used to explore other systems, including mixtures of chlorides or entirely different salts.}
}

@Article{Guo_MaterialsTodayEnergy_2021_v20_p100665,
    author =   {D. Guo and C. Li and K. Li and B. Shao and D. Chen and Y. Ma and J.
             Sun and X. Cao and W. Zeng and X. Chang},
    title =    {{The thermoelectric performance of new structure SnSe studied by
             quotient graph and deep learning potential}},
    journal =  {Materials Today Energy},
    year =     2021,
    volume =   20,
    pages =    100665,
    doi =      {10.1016/j.mtener.2021.100665},
}

@Article{Piaggi_JChemTheoryComput_2021_v17_p3065,
    author =   {Pablo M Piaggi and Athanassios Z Panagiotopoulos and Pablo G
             Debenedetti and Roberto Car},
    title =    {{Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN
             Functional}},
    journal =  {J. Chem. Theory Comput.},
    year =     2021,
    volume =   17,
    issue =    5,
    pages =    {3065--3077},
    annote =   {Machine learning models are rapidly becoming widely used to simulate
             complex physicochemical phenomena with ab initio accuracy. Here, we
             use one such model as well as direct density functional theory (DFT)
             calculations to investigate the phase equilibrium of water, hexagonal
             ice (Ih), and cubic ice (Ic), with an eye toward studying ice
             nucleation. The machine learning model is based on deep neural
             networks and has been trained on DFT data obtained using the SCAN
             exchange and correlation functional. We use this model to drive
             enhanced sampling simulations aimed at calculating a number of complex
             properties that are out of reach of DFT-driven simulations and then
             employ an appropriate reweighting procedure to compute the
             corresponding properties for the SCAN functional. This approach allows
             us to calculate the melting temperature of both ice polymorphs, the
             driving force for nucleation, the heat of fusion, the densities at the
             melting temperature, the relative stability of ices Ih and Ic, and
             other properties. We find a correct qualitative prediction of all
             properties of interest. In some cases, quantitative agreement with
             experiment is better than for state-of-the-art semiempirical
             potentials for water. Our results also show that SCAN correctly
             predicts that ice Ih is more stable than ice Ic.},
    doi =      {10.1021/acs.jctc.1c00041},
}

@article{Dai_JMaterSciTech_2021_v72_p8,
    title = {Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential},
    journal = {Journal of Materials Science & Technology},
    volume = {72},
    pages = {8-15},
    year = {2021},
    issn = {1005-0302},
    doi = {10.1016/j.jmst.2020.07.014},
    url = {https://www.sciencedirect.com/science/article/pii/S1005030220307714},
    author = {Fu-Zhi Dai and Yinjie Sun and Bo Wen and Huimin Xiang and Yanchun Zhou},
    keywords = {High entropy diborides, Machine learning potential, Thermal properties, Elastic properties, Molecular dynamics},
    abstract = {High entropy diborides are new categories of ultra-high temperature ceramics, which are believed promising candidates for applications in hypersonic vehicles. However, knowledge on high temperature thermal and mechanical properties of high entropy diborides is still lacking unit now. In this work, variations of thermal and elastic properties of high entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 with respect to temperature were predicted by molecular dynamics simulations. Firstly, a deep learning potential for Ti-Zr-Hf-Nb-Ta-B diboride system was fitted with its prediction error in energy and force respectively being 9.2 meV/atom and 208 meV/Å, in comparison with first-principles calculations. Then, temperature dependent lattice constants, anisotropic thermal expansions, anisotropic phonon thermal conductivities, and elastic properties of high entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 from 0 °C to 2400 °C were evaluated, where the predicted room temperature values agree well with experimental measurements. In addition, intrinsic lattice distortions of (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 were analyzed by displacements of atoms from their ideal positions, which are in an order of 10-3 Å and one order of magnitude smaller than those in (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C. It indicates that lattice distortions in (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 is not so severe as expected. With the new paradigm of machine learning potential, deep insight into high entropy materials can be achieved in the future, since the chemical and structural complexly in high entropy materials can be well handled by machine learning potential.}
}


@Article{Zhao_Ionics_2021_v27_p2079,
    author =   {Jia Zhao and Wenshuo Liang and Guimin Lu},
    title =    {{Theoretical prediction on the redox potentials of rare-earth ions by
             deep potentials}},
    journal =  {Ionics},
    year =     2021,
    volume =   27,
    issue =    5,
    pages =    {2079--2088},
    doi =      {10.1007/s11581-021-03988-0},
}

@Article{Jiang_ChinesePhysB_2021_v30_p050706,
    author =   {Wanrun Jiang and Yuzhi Zhang and Linfeng Zhang and Han Wang},
    title =    {{Accurate Deep Potential model for the Al{-}Cu{-}Mg
             alloy in the full concentration space*}},
    journal =  {Chinese Phys. B},
    year =     2021,
    volume =   30,
    issue =    5,
    pages =    050706,
    annote =   {<jats:p>Combining first-principles accuracy and empirical-potential
             efficiency for the description of the potential energy surface (PES)
             is the philosopher{\textquoteright}s stone for unraveling the nature
             of matter via atomistic simulation. This has been particularly
             challenging for multi-component alloy systems due to the complex and
             non-linear nature of the associated PES. In this work, we develop an
             accurate PES model for the Al{-}Cu{-}Mg system by
             employing deep potential (DP), a neural network based representation
             of the PES, and DP generator (DP-GEN), a concurrent-learning scheme
             that generates a compact set of <jats:italic>ab initio</jats:italic>
             data for training. The resulting DP model gives predictions consistent
             with first-principles calculations for various binary and ternary
             systems on their fundamental energetic and mechanical properties,
             including formation energy, equilibrium volume, equation of state,
             interstitial energy, vacancy and surface formation energy, as well as
             elastic moduli. Extensive benchmark shows that the DP model is ready
             and will be useful for atomistic modeling of the
             Al{-}Cu{-}Mg system within the full range of
             concentration.</jats:p>},
    doi =      {10.1088/1674-1056/abf134},
}

@Article{Shang_AIPAdvances_2021_v11_p035105,
    author =   {Honghui Shang and Haidi Wang},
    title =    {{Anharmonic Raman spectra simulation of crystals from deep neural
             networks}},
    journal =  {AIP Advances},
    year =     2021,
    volume =   11,
    issue =    3,
    pages =    035105,
    doi =      {10.1063/5.0040190},
}


@article{Liu_MatterRadiationExtremes_2021_v6_p2,
    author = {Liu,Qianrui  and Li,Junyi  and Chen,Mohan },
    title = {Thermal transport by electrons and ions in warm dense aluminum: A combined density functional theory and deep potential study},
    journal = {Matter and Radiation at Extremes},
    volume = {6},
    number = {2},
    pages = {026902},
    year = {2021},
    doi = {10.1063/5.0030123},
}


@article{Zhang_JPhysChemC_2021_v125_p3127,
    author = {Zhang, Chao and Sun, Yang and Wang, Hai-Di and Zhang, Feng and Wen, Tong-Qi and Ho, Kai-Ming and Wang, Cai-Zhuang},
    title = {Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential},
    journal = {J. Phys. Chem. C},
    volume = {125},
    number = {5},
    pages = {3127-3133},
    year = {2021},
    doi = {10.1021/acs.jpcc.0c08873},
}
@Article{Piaggi_MolecularPhysics_2021_v119_p1916634,
    author =   {Pablo M. Piaggi and Roberto Car},
    title =    {{Enhancing the formation of ionic defects to study the ice Ih/XI
             transition with molecular dynamics simulations}},
    journal =  {Molecular Physics},
    year =     2021,
    volume =   119,
    page = {e1916634},
    issue =    {19-20},
    doi =      {10.1080/00268976.2021.1916634},
}

@Article{Li_JChemTheoryComput_2022_v18_p2124,
    author =   {Chenghan Li and Francesco Paesani and Gregory A Voth},
    title =    {{Static and Dynamic Correlations in Water: Comparison of Classical Ab
             Initio Molecular Dynamics at Elevated Temperature with Path Integral
             Simulations at Ambient Temperature}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume =   18,
    issue =    4,
    pages =    {2124--2131},
    annote =   {It is a common practice in ab initio molecular dynamics (AIMD)
             simulations of water to use an elevated temperature to overcome the
             overstructuring and slow diffusion predicted by most current density
             functional theory (DFT) models. The simulation results obtained in
             this distinct thermodynamic state are then compared with experimental
             data at ambient temperature based on the rationale that a higher
             temperature effectively recovers nuclear quantum effects (NQEs) that
             are missing in the classical AIMD simulations. In this work, we
             systematically examine the foundation of this assumption for several
             DFT models as well as for the many-body MB-pol model. We find for the
             cases studied that a higher temperature does not correctly mimic NQEs
             at room temperature, which is especially manifest in significantly
             different three-molecule correlations as well as hydrogen bond
             dynamics. In many of these cases, the effects of NQEs are the opposite
             of the effects of carrying out the simulations at an elevated
             temperature.},
    doi =      {10.1021/acs.jctc.1c01223},
}

@Article{Feng_ComputationalMaterialsScience_2021_vNone_p111014,
    author =   {Taixi Feng and Jia Zhao and Wenshuo Liang and Guimin Lu},
    title =    {{Molecular dynamics simulations of lanthanum chloride by deep learning
             potential}},
    journal =  {Computational Materials Science},
    year =     2021,
    pages =    111014,
    doi =      {10.1016/j.commatsci.2021.111014},
}

@Article{Luo_GeochemPerspLet_2021_vNone_p19,
    author =   {H. Luo and B.B. Karki and D.B. Ghosh and H. Bao},
    title =    {{Diffusional fractionation of helium isotopes in silicate melts}},
    journal =  {Geochem. Persp. Let.},
    year =     2021,
    pages =    {19--22},
    doi =      {10.7185/geochemlet.2128},
}

@Article{Tang_ActaMaterialia_2021_v204_p116513,
    author =   {L. Tang and Z.J. Yang and T.Q. Wen and K.M. Ho and M.J. Kramer and
             C.Z. Wang},
    title =    {{Short- and medium-range orders in Al90Tb10 glass and their relation to
             the structures of competing crystalline phases}},
    journal =  {Acta Materialia},
    year =     2021,
    volume =   204,
    pages =    116513,
    doi =      {10.1016/j.actamat.2020.116513},
}

@Article{Pan_ComputationalMaterialsScience_2021_v187_p110055,
    author =   {Gechuanqi Pan and Jing Ding and Yunfei Du and Duu-Jong Lee and Yutong
             Lu},
    title =    {{A DFT accurate machine learning description of molten ZnCl2 and its
             mixtures: 2. Potential development and properties prediction of
             ZnCl2-NaCl-KCl ternary salt for CSP}},
    journal =  {Computational Materials Science},
    year =     2021,
    volume =   187,
    pages =    110055,
    doi =      {10.1016/j.commatsci.2020.110055},
}

@Article{Wu_PhysRevB_2021_v103_p024108,
    author =   {Jing Wu and Yuzhi Zhang and Linfeng Zhang and Shi Liu},
    title =    {{Deep learning of accurate force field of ferroelectric  HfO2}},
    journal =  {Phys. Rev. B},
    year =     2021,
    volume =   103,
    issue =    2,
    pages =    024108,
    doi =      {10.1103/PhysRevB.103.024108},
}

@Article{Liang_ACSApplMaterInterfaces_2021_v13_p4034,
    author =   {Wenshuo Liang and Guimin Lu and Jianguo Yu},
    title =    {{Machine-Learning-Driven Simulations on Microstructure and
             Thermophysical Properties of MgCl<sub>2</sub>{-}KCl Eutectic}},
    journal =  {ACS Appl. Mater. Interfaces},
    year =     2021,
    volume =   13,
    issue =    3,
    pages =    {4034--4042},
    annote =   {Theoretical studies on the MgCl2-KCl eutectic heavily rely on ab
             initio calculations based on density functional theory (DFT). However,
             neither large-scale nor long-time calculations are feasible in the
             framework of the ab initio method, which makes it challenging to
             accurately predict some properties. To address this issue, a scheme
             based on ab initio calculation, deep neural networks, and machine
             learning is introduced. By training on high-quality data sets
             generated by ab initio calculations, a deep potential (DP) is
             constructed to describe the interaction between atoms. This work shows
             that the DP enables higher efficiency and similar accuracy relative to
             DFT. By performing molecular dynamics simulations with DP, the
             microstructure and thermophysical properties of the MgCl2-KCl eutectic
             (32:68 mol %) are investigated. The structural evolution with
             temperature is analyzed through partial radial distribution functions,
             coordination numbers, angular distribution functions, and structural
             factors. Meanwhile, the estimated thermophysical properties are
             discussed, including density, thermal expansion coefficient, shear
             viscosity, self-diffusion coefficient, and specific heat capacity. It
             reveals that the Mg2+ ions in this system have a distorted tetrahedral
             geometry rather than an octahedral one (with vacancies). The
             microstructure of the MgCl2-KCl eutectic shows the feature of medium-
             range order, and this feature will be enhanced at a higher
             temperature. All predicted thermophysical properties are in good
             agreement with the experimental results. The hydrodynamic radius
             determined from the shear viscosity and self-diffusion coefficient
             shows that the Mg2+ ions have a strong local structure and diffuse as
             if with an intact coordination shell. Overall, this work provides a
             thorough understanding of the microstructure and enriches the data of
             the thermophysical properties of the MgCl2-KCl eutectic.},
    doi =      {10.1021/acsami.0c20665},
}

@Article{Li_InorgChemFront_2021_v8_p425,
    author =   {Han-Xiao Li and Xu-Yuan Zhou and Yue-Chao Wang and Hong Jiang},
    title =    {{Theoretical study of Na<sup>+</sup> transport in the solid-state
             electrolyte Na<sub>3</sub>OBr based on deep potential molecular
             dynamics}},
    journal =  {Inorg. Chem. Front.},
    year =     2021,
    volume =   8,
    issue =    2,
    pages =    {425--432},
    annote =   {<p>Deep potential molecular dynamics is used to study Na<sup>+</sup>
             transport in Na<sub>3</sub>OBr.</p>},
    doi =      {10.1039/D0QI00921K},
}

@Article{Yue_JChemPhys_2021_v154_p034111,
    author =   {Shuwen Yue and Maria Carolina Muniz and Marcos F {Calegari Andrade}
             and Linfeng Zhang and Roberto Car and Athanassios Z Panagiotopoulos},
    title =    {{When do short-range atomistic machine-learning models fall short?}},
    journal =  {J. Chem. Phys.},
    year =     2021,
    volume =   154,
    issue =    3,
    pages =    034111,
    annote =   {We explore the role of long-range interactions in atomistic machine-
             learning models by analyzing the effects on fitting accuracy, isolated
             cluster properties, and bulk thermodynamic properties. Such models
             have become increasingly popular in molecular simulations given their
             ability to learn highly complex and multi-dimensional interactions
             within a local environment; however, many of them fundamentally lack a
             description of explicit long-range interactions. In order to provide a
             well-defined benchmark system with precisely known pairwise
             interactions, we chose as the reference model a flexible version of
             the Extended Simple Point Charge (SPC/E) water model. Our analysis
             shows that while local representations are sufficient for predictions
             of the condensed liquid phase, the short-range nature of machine-
             learning models falls short in representing cluster and vapor phase
             properties. These findings provide an improved understanding of the
             role of long-range interactions in machine learning models and the
             regimes where they are necessary.},
    doi =      {10.1063/5.0031215},
}

@Article{Pegolo_npjComputMater_2022_v8_p24,
    author =   {Paolo Pegolo and Stefano Baroni and Federico Grasselli},
    title =    {{Temperature- and vacancy-concentration-dependence of heat transport in
             Li3ClO from multi-method numerical simulations}},
    journal =  {npj Comput Mater},
    year =     2022,
    volume =   8,
    issue =    1,
    pages =    24,
    annote =   {<jats:title>Abstract</jats:title><jats:p>Despite governing heat
             management in any realistic device, the microscopic mechanisms of heat
             transport in all-solid-state electrolytes are poorly known: existing
             calculations, all based on simplistic semi-empirical models, are
             unreliable for superionic conductors and largely overestimate their
             thermal conductivity. In this work, we deploy a combination of state-
             of-the-art methods to calculate the thermal conductivity of a
             prototypical Li-ion conductor, the Li<jats:sub>3</jats:sub>ClO
             antiperovskite. By leveraging ab initio, machine learning, and force-
             field descriptions of interatomic forces, we are able to reveal the
             massive role of anharmonic interactions and diffusive defects on the
             thermal conductivity and its temperature dependence, and to eventually
             embed their effects into a simple rationale which is likely applicable
             to a wide class of ionic conductors.</jats:p>},
    doi =      {10.1038/s41524-021-00693-4},
}

@article{Dai_JMaterSciTech_2022_v123_p26,
    title = {Grain boundary segregation induced strong UHTCs at elevated temperatures: A universal mechanism from conventional UHTCs to high entropy UHTCs},
    journal = {Journal of Materials Science & Technology},
    volume = {123},
    pages = {26-33},
    year = {2022},
    issn = {1005-0302},
    doi = {10.1016/j.jmst.2021.12.074},
    url = {https://www.sciencedirect.com/science/article/pii/S100503022200264X},
    author = {Fu-Zhi Dai and Bo Wen and Yinjie Sun and Yixiao Ren and Huimin Xiang and Yanchun Zhou},
    keywords = {UHTCs, High entropy ceramics, Grain boundary segregation, High-temperature strength, Machine learning potential},
    abstract = {Ultra-high temperature ceramics (UHTCs) exhibit a unique combination of excellent properties, including ultra-high melting point, excellent chemical stability, and good oxidation resistance, which make them promising candidates for aerospace and nuclear applications. However, the degradation of high-temperature strength is one of the main limitations for their ultra-high temperature applications. Thus, searching for mechanisms that can help to develop high-performance UHTCs with good high-temperature mechanical properties is urgently needed. To achieve this goal, grain boundary segregation of a series of carbides, including conventional, medium entropy, and high entropy transition metal carbides, i.e., Zr0.95W0.05C, TiZrHfC3, ZrHfNbTaC4, TiZrHfNbTaC5, were studied by atomistic simulations with a fitted Deep Potential (DP), and the effects of segregation on grain boundary strength were emphasized. For all the studied carbides, grain boundary segregations are realized, which are dominated by the atomic size effect. In addition, tensile simulations indicate that grain boundaries (GBs) will usually be strengthened due to segregation. Our simulation results reveal that grain boundary segregation may be a universal mechanism in enhancing the high-temperature strength of both conventional UHTCs and medium/high entropy UHTCs, since GBs play a key role in controlling the fracture of UHTCs at elevated temperatures.}
}

@Article{Fu_JournalofEnergyChemistry_2022_v70_p59,
    author =   {Zhong-Heng Fu and Xiang Chen and Nan Yao and Xin Shen and Xia-Xia Ma
             and Shuai Feng and Shuhao Wang and Rui Zhang and Linfeng Zhang and
             Qiang Zhang},
    title =    {{The chemical origin of temperature-dependent lithium-ion concerted
             diffusion in sulfide solid electrolyte Li10GeP2S12}},
    journal =  {Journal of Energy Chemistry},
    year =     2022,
    volume =   70,
    pages =    {59--66},
    doi =      {10.1016/j.jechem.2022.01.018},
}

@Article{Sun_JournalofMolecularLiquids_2022_v353_p118787,
    author =   {Yuhan Sun and Min Tan and Tao Li and Junguo Li and Bo Shang},
    title =    {{Study on the structural properties of refining slags by molecular
             dynamics with deep learning potential}},
    journal =  {Journal of Molecular Liquids},
    year =     2022,
    volume =   353,
    pages =    118787,
    doi =      {10.1016/j.molliq.2022.118787},
}

@Article{Huang_EnergyandAI_2022_v8_p100135,
    author =   {Xiaona Huang and Yidi Shen and Qi An},
    title =    {{Nanotwinning induced decreased lattice thermal conductivity of high
             temperature thermoelectric boron subphosphide (B12P2) from deep
             learning potential simulations}},
    journal =  {Energy and AI},
    year =     2022,
    volume =   8,
    pages =    100135,
    doi =      {10.1016/j.egyai.2022.100135},
}

@Article{Zhang_JChemPhys_2022_v156_p124107,
    author =   {Linfeng Zhang and Han Wang and Maria Carolina Muniz and Athanassios Z
             Panagiotopoulos and Roberto Car and Weinan E},
    title =    {{A deep potential model with long-range electrostatic interactions}},
    journal =  {J. Chem. Phys.},
    year =     2022,
    volume =   156,
    issue =    12,
    pages =    124107,
    annote =   {Machine learning models for the potential energy of multi-atomic
             systems, such as the deep potential (DP) model, make molecular
             simulations with the accuracy of quantum mechanical density functional
             theory possible at a cost only moderately higher than that of
             empirical force fields. However, the majority of these models lack
             explicit long-range interactions and fail to describe properties that
             derive from the Coulombic tail of the forces. To overcome this
             limitation, we extend the DP model by approximating the long-range
             electrostatic interaction between ions (nuclei + core electrons) and
             valence electrons with that of distributions of spherical Gaussian
             charges located at ionic and electronic sites. The latter are
             rigorously defined in terms of the centers of the maximally localized
             Wannier distributions, whose dependence on the local atomic
             environment is modeled accurately by a deep neural network. In the DP
             long-range (DPLR) model, the electrostatic energy of the Gaussian
             charge system is added to short-range interactions that are
             represented as in the standard DP model. The resulting potential
             energy surface is smooth and possesses analytical forces and virial.
             Missing effects in the standard DP scheme are recovered, improving on
             accuracy and predictive power. By including long-range electrostatics,
             DPLR correctly extrapolates to large systems the potential energy
             surface learned from quantum mechanical calculations on smaller
             systems. We illustrate the approach with three examples: the potential
             energy profile of the water dimer, the free energy of interaction of a
             water molecule with a liquid water slab, and the phonon dispersion
             curves of the NaCl crystal.},
    doi =      {10.1063/5.0083669},
}

@Article{Sun_ACSApplMaterInterfaces_2022_v14_p11493,
    author =   {Jie Sun and Cunzhi Zhang and Zhonghua Yang and Yiheng Shen and Ming Hu
             and Qian Wang},
    title =    {{Four-Phonon Scattering Effect and Two-Channel Thermal Transport in
             Two-Dimensional Paraelectric SnSe}},
    journal =  {ACS Appl. Mater. Interfaces},
    year =     2022,
    volume =   14,
    issue =    9,
    pages =    {11493--11499},
    annote =   {In recent years, increased attention has been paid to the study of
             four-phonon interactions and diffusion transport in three-dimensional
             (3D) thermoelectric materials because they play an essential role in
             understanding the thermal transport process. In this work, we study
             four-phonon scattering and diffusion transport in two-dimensional (2D)
             thermoelectric materials using the paraelectric phase of 2D SnSe as an
             example. The inherent soft phonon modes are treated by the self-
             consistent phonon theory, which considers the temperature-induced
             renormalization of the phonons. Based on density functional theory and
             the Peierls-Boltzmann transport equation for phonons, we show that the
             four-phonon interactions can reduce the thermal conductivity of the 2D
             SnSe sheet by nearly 40% due to the collapse of soft optical modes,
             and the contribution of diffusion transport to the total thermal
             conductivity accounts for 14% at a high temperature of 800 K due to
             the short phonon mean free path approaching the Ioffe-Regel limit,
             suggesting the two-channel transport in this system. The results are
             further confirmed by using the machine learning-assisted molecular
             dynamics simulations. This work provides a new insight into the
             physical mechanisms for thermal transport in 2D systems with strong
             anharmonic effects.},
    doi =      {10.1021/acsami.1c24488},
}

@Article{Han_BriefBioinform_2022_v23_pNone,
    author =   {Yanqiang Han and Zhilong Wang and An Chen and Imran Ali and Junfei Cai
             and Simin Ye and Jinjin Li},
    title =    {{An inductive transfer learning force field (ITLFF) protocol builds
             protein force fields in seconds}},
    journal =  {Brief. Bioinform.},
    year =     2022,
    volume =   23,
    issue =    2,
    annote =   {Accurate simulation of protein folding is a unique challenge in
             understanding the physical process of protein folding, with important
             implications for protein design and drug discovery. Molecular dynamics
             simulation strongly requires advanced force fields with high accuracy
             to achieve correct folding. However, the current force fields are
             inaccurate, inapplicable and inefficient. We propose a machine
             learning protocol, the inductive transfer learning force field
             (ITLFF), to construct protein force fields in seconds with any level
             of accuracy from a small dataset. This process is achieved by
             incorporating an inductive transfer learning algorithm into deep
             neural networks, which learn knowledge of any high-level calculations
             from a large dataset of low-level method. Here, we use a double-hybrid
             density functional theory (DFT) as a case functional, but ITLFF is
             suitable for any high-precision functional. The performance of the
             selected 18 proteins indicates that compared with the fragment-based
             double-hybrid DFT algorithm, the force field constructed by ITLFF
             achieves considerable accuracy with a mean absolute error of
             0.0039{~}kcal/mol/atom for energy and a root mean square error of
             2.57{~}$\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more
             than 30{~}000 times faster and obtains more significant efficiency
             benefits as the system increases. The outstanding performance of ITLFF
             provides promising prospects for accurate and efficient protein
             dynamic simulations and makes an important step toward protein folding
             simulation. Due to the ability of ITLFF to utilize the knowledge
             acquired in one task to solve related problems, it is also applicable
             for various problems in biology, chemistry and material science.},
    doi =      {10.1093/bib/bbab590},
}

@article{Liu_JVacuumSciTech_2022_v40_p023205,
    author = {Liu,Da-Jiang  and Evans,James W. },
    title = {Sulfur-enhanced dynamics of coinage metal(111) surfaces: Step edges versus terraces as locations for metal-sulfur complex formation},
    journal = {Journal of Vacuum Science \& Technology A},
    volume = {40},
    number = {2},
    pages = {023205},
    year = {2022},
    doi = {10.1116/6.0001408},
}

@Article{Wang_ModellingSimulMaterSciEng_2022_v30_p025003,
    author =   {YiNan Wang and LinFeng Zhang and Ben Xu and XiaoYang Wang and Han Wang},
    title =    {{A generalizable machine learning potential of Ag{-}Au
             nanoalloys and its application to surface reconstruction, segregation
             and diffusion}},
    journal =  {Modelling Simul. Mater. Sci. Eng.},
    year =     2022,
    volume =   30,
    issue =    2,
    pages =    025003,
    annote =   {<jats:title>Abstract</jats:title>                <jats:p>Owing to the
             excellent catalytic properties of Ag{-}Au binary nanoalloys,
             nanostructured Ag{-}Au, such as Ag{-}Au
             nanoparticles and nanopillars, has been under intense investigation.
             To achieve high accuracy in molecular simulations of Ag{-}Au
             nanoalloys, the surface properties must be modeled with first-
             principles precision. In this work, we constructed a generalizable
             machine learning interatomic potential for Ag{-}Au
             nanoalloys based on deep neural networks trained from a database
             constructed with first-principles calculations. This potential is
             highlighted by the accurate prediction of Au (111) surface
             reconstruction and the segregation of Au toward the Ag{-}Au
             nanoalloy surface, where the empirical force field (EFF) failed in
             both cases. Moreover, regarding the adsorption and diffusion of
             adatoms on surfaces, the overall performance of our potential is
             better than the EFFs. We stress that the reported surface properties
             are blind to the potential modeling in the sense that none of the
             surface configurations is explicitly included in the training
             database; therefore, the reported potential is expected to have a
             strong generalization ability to a wide range of properties and to
             play a key role in investigating nanostructured Ag{-}Au
             evolution, where accurate descriptions of free surfaces are
             necessary.</jats:p>},
    doi =      {10.1088/1361-651X/ac4002},
}

@Article{Ryltsev_JournalofMolecularLiquids_2022_v349_p118181,
    author =   {R.E. Ryltsev and N.M. Chtchelkatchev},
    title =    {{Deep machine learning potentials for multicomponent metallic melts:
             Development, predictability and compositional transferability}},
    journal =  {Journal of Molecular Liquids},
    year =     2022,
    volume =   349,
    pages =    118181,
    doi =      {10.1016/j.molliq.2021.118181},
}

@Article{Xie_JPhysCondensMatter_2022_v34_p075402,
    author =   {Kun Xie and Chong Qiao and Hong Shen and Riyi Yang and Ming Xu and
             Chao Zhang and Yuxiang Zheng and Rongjun Zhang and Liangyao Chen and
             Kai-Ming Ho and Cai-Zhuang Wang and Songyou Wang},
    title =    {{Neural network potential for Zr{-}Rh system by machine
             learning}},
    journal =  {J. Phys. Condens. Matter},
    year =     2022,
    volume =   34,
    issue =    7,
    pages =    075402,
    annote =   {Zr-Rh metallic glass has enabled its many applications in vehicle
             parts, sports equipment and so on due to its outstanding performance
             in mechanical property, but the knowledge of the microstructure
             determining the superb mechanical property remains yet insufficient.
             Here, we develop a deep neural network potential of Zr-Rh system by
             using machine learning, which breaks the dilemma between the accuracy
             and efficiency in molecular dynamics simulations, and greatly improves
             the simulation scale in both space and time. The results show that the
             structural features obtained from the neural network method are in
             good agreement with the cases inab initiomolecular dynamics
             simulations. Furthermore, we build a large model of 5400 atoms to
             explore the influences of simulated size and cooling rate on the melt-
             quenching process of Zr77Rh23. Our study lays a foundation for
             exploring the complex structures in amorphous Zr77Rh23, which is of
             great significance for the design{~}and practical application.},
    doi =      {10.1088/1361-648X/ac37dc},
}

@Article{Guo_JournalofMolecularLiquids_2022_v348_p118380,
    author =   {Di Guo and Jia Zhao and Wenshuo Liang and Guimin Lu},
    title =    {{Molecular dynamics simulation of molten strontium chloride based on
             deep potential}},
    journal =  {Journal of Molecular Liquids},
    year =     2022,
    volume =   348,
    pages =    118380,
    doi =      {10.1016/j.molliq.2021.118380},
}


@article{Urata_JPhysChemC_2022_v126_p4,
    author = {Urata, Shingo and Nakamura, Nobuhiro and Tada, Tomofumi and Tan, Aik Rui and Gómez-Bombarelli, Rafael and Hosono, Hideo},
    title = {Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with Force-Matching and Neural Network Potentials},
    journal = {J. Phys. Chem. C},
    volume = {126},
    number = {4},
    pages = {2264-2275},
    year = {2022},
    doi = {10.1021/acs.jpcc.1c10300},
}
@Article{Zhou_CementandConcreteResearch_2022_v152_p106685,
    author =   {Yang Zhou and Haojie Zheng and Weihuan Li and Tao Ma and Changwen Miao},
    title =    {{A deep learning potential applied in tobermorite phases and extended
             to calcium silicate hydrates}},
    journal =  {Cement and Concrete Research},
    year =     2022,
    volume =   152,
    pages =    106685,
    doi =      {10.1016/j.cemconres.2021.106685},
}

@Article{Balyakin_ComputationalMaterialsScience_2022_v202_p110963,
    author =   {I.A. Balyakin and S.I. Sadovnikov},
    title =    {{Deep learning potential for superionic phase of Ag2S}},
    journal =  {Computational Materials Science},
    year =     2022,
    volume =   202,
    pages =    110963,
    doi =      {10.1016/j.commatsci.2021.110963},
}

@Article{Gu_ScienceBulletin_2022_v67_p29,
    author =   {Qiangqiang Gu and Linfeng Zhang and Ji Feng},
    title =    {{Neural network representation of electronic structure from ab initio
             molecular dynamics}},
    journal =  {Science Bulletin},
    year =     2022,
    volume =   67,
    issue =    1,
    pages =    {29--37},
    doi =      {10.1016/j.scib.2021.09.010},
}

@Article{Zhang_PhysRevLett_2018_v120_p143001,
    author =   {Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E},
    title =    {{Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy
             of Quantum Mechanics}},
    journal =  {Phys. Rev. Lett.},
    year =     2018,
    volume =   120,
    issue =    14,
    pages =    143001,
    annote =   {We introduce a scheme for molecular simulations, the deep potential
             molecular dynamics (DPMD) method, based on a many-body potential and
             interatomic forces generated by a carefully crafted deep neural
             network trained with ab{~}initio data. The neural network model
             preserves all the natural symmetries in the problem. It is first-
             principles based in the sense that there are no ad{~}hoc components
             aside from the network model. We show that the proposed scheme
             provides an efficient and accurate protocol in a variety of systems,
             including bulk materials and molecules. In all these cases, DPMD gives
             results that are essentially indistinguishable from the original data,
             at a cost that scales linearly with system size.},
    doi =      {10.1103/PhysRevLett.120.143001},
}

@article{He_PhysRevB_2022_v105_p064104,
  title = {Structural phase transitions in $\mathrm{SrTi}{\mathrm{O}}_{3}$ from deep potential molecular dynamics},
  author = {He, Ri and Wu, Hongyu and Zhang, Linfeng and Wang, Xiaoxu and Fu, Fangjia and Liu, Shi and Zhong, Zhicheng},
  journal = {Phys. Rev. B},
  volume = {105},
  issue = {6},
  pages = {064104},
  numpages = {10},
  year = {2022},
  month = {Feb},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevB.105.064104},
}

@Article{Yang_Chemphyschem_2022_v23_pe202100841,
    author =   {Weijie Yang and Jiajia Li and Xuelu Chen and Yajun Feng and Chongchong
             Wu and Ian D Gates and Zhengyang Gao and Xunlei Ding and Jianxi Yao
             and Hao Li},
    title =    {{Exploring the Effects of Ionic Defects on the Stability of CsPbI
             <sub>3</sub>             with a Deep Learning Potential}},
    journal =  {Chemphyschem},
    year =     2022,
    volume =   23,
    issue =    7,
    pages =    {e202100841},
    annote =   {Inorganic metal halide perovskites, such as CsPbI3 , have recently
             drawn extensive attention due to their excellent optical properties
             and high photoelectric efficiencies. However, the structural
             instability originating from inherent ionic defects leads to a sharp
             drop in the photoelectric efficiency, which significantly limits their
             applications in solar cells. The instability induced by ionic defects
             remains unresolved due to its complicated reaction process. Herein, to
             explore the effects of ionic defects on stability, we develop a deep
             learning potential for a CsPbI3 ternary system based upon density
             functional theory (DFT) calculated data for large-scale molecular
             dynamics (MD) simulations. By exploring 2.4 million configurations, of
             which 7,730 structures are used for the training set, the deep
             learning potential shows an accuracy approaching DFT-level.
             Furthermore, MD simulations with a 5,000-atom system and a one
             nanosecond timeframe are performed to explore the effects of bulk and
             surface defects on the stability of CsPbI3 . This deep learning
             potential based MD simulation provides solid evidence together with
             the derived radial distribution functions, simulated diffraction of
             X-rays, instability temperature, molecular trajectory, and
             coordination number for revealing the instability mechanism of CsPbI3
             . Among bulk defects, Cs defects have the most significant influence
             on the stability of CsPbI3 with a defect tolerance concentration of
             0.32{\,}%, followed by Pb and I defects. With regards to surface
             defects, Cs defects have the largest impact on the stability of CsPbI3
             when the defect concentration is less than 15{\,}%, whereas Pb defects
             act play a dominant role for defect concentrations exceeding 20{\,}%.
             Most importantly, this machine-learning-based MD simulation strategy
             provides a new avenue to explore the ionic defect effects on the
             stability of perovskite-like materials, laying a theoretical
             foundation for the design of stable perovskite materials.},
    doi =      {10.1002/cphc.202100841},
}

@Article{Zhang_NatCommun_2022_v13_p822,
    author =   {Chunyi Zhang and Shuwen Yue and Athanassios Z Panagiotopoulos and
             Michael L Klein and Xifan Wu},
    title =    {{Dissolving salt is not equivalent to applying a pressure on water}},
    journal =  {Nat. Commun.},
    year =     2022,
    volume =   13,
    issue =    1,
    pages =    822,
    annote =   {Salt water is ubiquitous, playing crucial roles in geological and
             physiological processes. Despite centuries of investigations, whether
             or not water's structure is drastically changed by dissolved ions is
             still debated. Based on density functional theory, we employ machine
             learning based molecular dynamics to model sodium chloride, potassium
             chloride, and sodium bromide solutions at different concentrations.
             The resulting reciprocal-space structure factors agree quantitatively
             with neutron diffraction data. Here we provide clear evidence that the
             ions in salt water do not distort the structure of water in the same
             way as neat water responds to elevated pressure. Rather, the computed
             structural changes are restricted to the ionic first solvation shells
             intruding into the hydrogen bond network, beyond which the oxygen
             radial-distribution function does not undergo major change relative to
             neat water. Our findings suggest that the widely cited pressure-like
             effect on the solvent in Hofmeister series ionic solutions should be
             carefully revisited.},
    PMCID =    {PMC8831556},
    doi =      {10.1038/s41467-022-28538-8},
}

@Article{Mo_npjComputMater_2022_v8_p107,
    author =   {Pinghui Mo and Chang Li and Dan Zhao and Yujia Zhang and Mengchao Shi
             and Junhua Li and Jie Liu},
    title =    {{Accurate and efficient molecular dynamics based on machine learning
             and non von Neumann architecture}},
    journal =  {npj Comput Mater},
    year =     2022,
    volume =   8,
    issue =    1,
    pages =    107,
    annote =   {<jats:title>Abstract</jats:title><jats:p>Force field-based classical
             molecular dynamics (CMD) is efficient but its potential energy surface
             (PES) prediction error can be very large. Density functional theory
             (DFT)-based ab-initio molecular dynamics (AIMD) is accurate but
             computational cost limits its applications to small systems. Here, we
             propose a molecular dynamics (MD) methodology which can simultaneously
             achieve both AIMD-level high accuracy and CMD-level high efficiency.
             The high accuracy is achieved by exploiting deep neural network
             (DNN){\textquoteright}s arbitrarily-high precision to fit PES. The
             high efficiency is achieved by deploying multiplication-less DNN on a
             carefully-optimized special-purpose non von Neumann (NvN) computer to
             mitigate the performance-limiting data shuttling (i.e.,
             {\textquoteleft}memory wall bottleneck{\textquoteright}). By testing
             on different molecules and bulk systems, we show that the proposed MD
             methodology is generally-applicable to various MD tasks. The proposed
             MD methodology has been deployed on an in-house computing server based
             on reconfigurable field programmable gate array (FPGA), which is
             freely available at <jats:ext-link
             xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri"
             xlink:href="http://nvnmd.picp.vip">http://nvnmd.picp.vip</jats:ext-
             link>.</jats:p>},
    doi =      {10.1038/s41524-022-00773-z},
}
@Article{McGilvryJames_MRSAdvances_2022_v7_p468,
    author =   {Tyler McGilvry-James and Bikash Timalsina and Marium Mostafiz Mou and
             Ridwan Sakidja},
    title =    {{Deep potential development of transition-metal-rich carbides}},
    journal =  {MRS Advances},
    year =     2022,
    volume =   7,
    issue =    22,
    pages =    {468--473},
    doi =      {10.1557/s43580-022-00289-0},
}
@Article{Balyakin_DoklPhysChem_2022_v502_p11,
    author =   {I. A. Balyakin and A. A. Rempel},
    title =    {{Atomistic Calculation of the Melting Point of the High-Entropy Cantor
             Alloy CoCrFeMnNi}},
    journal =  {Dokl Phys Chem},
    year =     2022,
    volume =   502,
    issue =    1,
    pages =    {11--17},
    doi =      {10.1134/S0012501622010018},
}
@Article{Gupta_AdvancedEnergyMaterials_2022_vNone_p2200596,
    author =   {Mayanak K. Gupta and Jingxuan Ding and Dipanshu Bansal and Douglas L.
             Abernathy and Georg Ehlers and Naresh C. Osti and Wolfgang G. Zeier
             and Olivier Delaire},
    title =    {{Strongly Anharmonic Phonons and Their Role in Superionic Diffusion and
             Ultralow Thermal Conductivity of Cu             <sub>7</sub>
             PSe             <sub>6</sub>}},
    journal =  {Advanced Energy Materials},
    year =     2022,
    pages =    2200596,
    doi =      {10.1002/aenm.202200596},
    volume = 12,
    issue = 23,
}
@Article{delaPuente_JAmChemSoc_2022_vNone_pNone,
    author =   {Miguel {de la Puente} and Rolf David and Axel Gomez and Damien Laage},
    title =    {{Acids at the Edge: Why Nitric and Formic Acid Dissociations at
             Air{\textendash}Water Interfaces Depend on Depth and on Interface
             Specific Area}},
    journal =  {J. Am. Chem. Soc.},
    year =     2022,
    doi =      {10.1021/jacs.2c03099},
    volume = 144,
    issue=23,
    pages={10524--10529},
}

@Article{Nakata_ACSApplMaterInterfaces_2022_v14_p26116,
    author =   {Hiroya Nakata and Michael {Filatov Gulak} and Cheol Ho Choi},
    title =    {{Accelerated Deep Learning Dynamics for Atomic Layer Deposition of
             Al(Me)<sub>3</sub> and Water on OH/Si(111)}},
    journal =  {ACS Appl. Mater. Interfaces},
    year =     2022,
    volume =   14,
    issue =    22,
    pages =    {26116--26127},
    annote =   {Knowledge of the detailed mechanism behind the atomic layer deposition
             (ALD) can greatly facilitate the optimization of the manufacturing
             process. Computational modeling can potentially foster the
             understanding; however, the presently available capabilities of the
             accurate ab initio computational techniques preclude their application
             to modeling surface processes occurring on a long time scale, such as
             ALD. Although the situation can be greatly improved using machine
             learning (ML), this technique requires an enormous amount of data for
             training datasets. Here, we propose an iterative protocol for
             optimizing ML training datasets and apply ML-assisted ab initio
             calculations to model surface reactions occurring during the
             Al(Me)3/H2O ALD process on the OH-terminated Si (111) surface. The
             protocol uses a recently developed low-dimensional projection
             technique (TDUS), greatly reducing the amount of information required
             to achieve high accuracy (ca. 1 kcal/mol or less) of the developed ML
             models. The resulting free energy landscapes reveal fine details of
             various aspects of the target ALD process, such as the surface proton
             transfer, zwitterionic surface configurations, elimination-
             addition/addition-elimination, and SN2 reactions as well as the role
             of the surface entropic and temperature effects. Simulations of
             adsorption dynamics predict that the maximum physisorption rate of ca.
             70% is achieved at the incidence velocity urms of the reactants in the
             range of 15-20 {\r{A}}/ps. Hence, the proposed protocol furnishes a
             very effective tool to study complex chemical reaction dynamics at a
             much reduced computational cost.},
    doi =      {10.1021/acsami.2c01768},
}

@Article{Zhang_PhysRevMaterials_2022_v6_p063802,
    author =   {Chao Zhang and Ling Tang and Yang Sun and Kai-Ming Ho and Renata M.
             Wentzcovitch and Cai-Zhuang Wang},
    title =    {{Deep machine learning potential for atomistic simulation of Fe-Si-O
             systems under Earth's outer core conditions}},
    journal =  {Phys. Rev. Materials},
    year =     2022,
    volume =   6,
    issue =    6,
    pages =    063802,
    doi =      {10.1103/PhysRevMaterials.6.063802},
}

@Article{Liu_JChemPhys_2022_v156_p204106,
    author =   {Da-Jiang Liu and James W Evans},
    title =    {{Reaction processes at step edges on S-decorated Cu(111) and Ag(111)
             surfaces: MD analysis utilizing machine learning derived potentials}},
    journal =  {J. Chem. Phys.},
    year =     2022,
    volume =   156,
    issue =    20,
    pages =    204106,
    annote =   {A variety of complexation, reconstruction, and sulfide formation
             processes can occur at step edges on the {111} surfaces of coinage
             metals (M) in the presence of adsorbed S under ultra-high vacuum
             conditions. Given the cooperative many-atom nature of these reaction
             processes, Molecular Dynamics (MD) simulation of the associated
             dynamics is instructive. However, only quite restricted Density
             Functional Theory (DFT)-level ab initio MD is viable. Thus, for M = Ag
             and Cu, we instead utilize the DeePMD framework to develop machine-
             learning derived potentials, retaining near-DFT accuracy for the M-S
             systems, which should have broad applicability. These potentials are
             validated by comparison with DFT predictions for various key
             quantities related to the energetics of S on M(111) surfaces. The
             potentials are then utilized to perform extensive MD simulations
             elucidating the above diverse restructuring and reaction processes at
             step edges. Key observations from MD simulations include the formation
             of small metal-sulfur complexes, especially MS2; development of a
             local reconstruction at A-steps featuring an S-decorated {100} motif;
             and 3D sulfide formation. Additional analysis yields further
             information on the kinetics for metal-sulfur complex formation, where
             these complexes can strongly enhance surface mass transport, and on
             the propensity for sulfide formation.},
    doi =      {10.1063/5.0089210},
}

@Article{Chu_JPhysChemLett_2022_v13_p4052,
    author =   {Qingzhao Chu and Kai H Luo and Dongping Chen},
    title =    {{Exploring Complex Reaction Networks Using Neural Network-Based
             Molecular Dynamics Simulation}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2022,
    volume =   13,
    issue =    18,
    pages =    {4052--4057},
    annote =   {Ab initio molecular dynamics (AIMD) is an established method for
             revealing the reactive dynamics of complex systems. However, the high
             computational cost of AIMD restricts the explorable length and time
             scales. Here, we develop a fundamentally different approach using
             molecular dynamics simulations powered by a neural network potential
             to investigate complex reaction networks. This potential is trained
             via a workflow combining AIMD and interactive molecular dynamics in
             virtual reality to accelerate the sampling of rare reactive processes.
             A panoramic visualization of the complex reaction networks for
             decomposition of a novel high explosive (ICM-102) is achieved without
             any predefined reaction coordinates. The study leads to the discovery
             of new pathways that would be difficult to uncover if established
             methods were employed. These results highlight the power of neural
             network-based molecular dynamics simulations in exploring complex
             reaction mechanisms under extreme conditions at the ab initio level,
             pushing the limit of theoretical and computational chemistry toward
             the realism and fidelity of experiments.},
    doi =      {10.1021/acs.jpclett.2c00647},
}

@Article{Zeng_PhysRevB_2022_v105_p174109,
    author =   {Qiyu Zeng and Bo Chen and Xiaoxiang Yu and Shen Zhang and Dongdong
             Kang and Han Wang and Jiayu Dai},
    title =    {{Towards large-scale and spatiotemporally resolved diagnosis of
             electronic density of states by deep learning}},
    journal =  {Phys. Rev. B},
    year =     2022,
    volume =   105,
    issue =    17,
    pages =    174109,
    doi =      {10.1103/PhysRevB.105.174109},
}

@Article{Shi_ChemSci_2022_vNone_pNone,
    author =   {Yu Shi and Stephen T. Lam and Thomas{~}L. Beck},
    title =    {{Deep neural network based quantum simulations and quasichemical theory
             for accurate modeling of molten salt thermodynamics}},
    journal =  {Chem. Sci.},
    year =     2022,
    annote =   {<jats:p>Solvation thermodynamics in molten salt is accurately and
             efficiently predicted by combining <jats:italic>ab
             initio</jats:italic> molecular dynamics (AIMD) simulations, deep
             neural network interatomic potentials (NNIP), and quasichemical theory
             (QCT).</jats:p>},
    doi =      {10.1039/D2SC02227C},
}

@Article{Achar_JChemTheoryComput_2022_v18_p3593,
    author =   {Siddarth K Achar and Jacob J Wardzala and Leonardo Bernasconi and
             Linfeng Zhang and J Karl Johnson},
    title =    {{Combined Deep Learning and Classical Potential Approach for Modeling
             Diffusion in UiO-66}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume =   18,
    issue =    6,
    pages =    {3593--3606},
    annote =   {Modeling of diffusion of adsorbates through porous materials with
             atomistic molecular dynamics (MD) can be a challenging task if the
             flexibility of the adsorbent needs to be included. This is because
             potentials need to be developed that accurately account for the motion
             of the adsorbent in response to the presence of adsorbate molecules.
             In this work, we show that it is possible to use accurate machine
             learning atomistic potentials for metal-organic frameworks in concert
             with classical potentials for adsorbates to accurately compute
             diffusivities though a hybrid potential approach. As a proof-of-
             concept, we have developed an accurate deep learning potential (DP)
             for UiO-66, a metal-organic framework, and used this DP to perform
             hybrid potential simulations, modeling diffusion of neon and xenon
             through the crystal. The adsorbate-adsorbate interactions were modeled
             with Lennard-Jones (LJ) potentials, the adsorbent-adsorbent
             interactions were described by the DP, and the adsorbent-adsorbate
             interactions used LJ cross-interactions. Thus, our hybrid potential
             allows for adsorbent-adsorbate interactions with classical potentials
             but models the response of the adsorbent to the presence of the
             adsorbate through near-DFT accuracy DPs. This hybrid approach does not
             require refitting the DP for new adsorbates. We calculated self-
             diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ
             approach, and from two different classical potentials for UiO-66. Our
             DP/LJ results are in excellent agreement with DFT-MD. We modeled
             diffusion of Xe in UiO-66 with DP/LJ and a classical potential.
             Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of
             Ne, so it is not computationally feasible to compute Xe diffusion with
             DFT-MD. Our hybrid DP-classical potential approach can be applied to
             other MOFs and other adsorbates, making it possible to use an accurate
             DP generated from DFT simulations of an empty adsorbent in concert
             with existing classical potentials for adsorbates to model adsorption
             and diffusion within the porous material, including adsorbate-induced
             changes to the framework.},
    doi =      {10.1021/acs.jctc.2c00010},
}

@Article{Xie_JChemTheoryComput_2022_v18_p3795,
    author =   {Jun-Zhong Xie and Xu-Yuan Zhou and Dong Luan and Hong Jiang},
    title =    {{Machine Learning Force Field Aided Cluster Expansion Approach to
             Configurationally Disordered Materials: Critical Assessment of
             Training Set Selection and Size Convergence}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume =   18,
    issue =    6,
    pages =    {3795--3804},
    annote =   {Cluster expansion (CE) is a powerful theoretical tool to study the
             configuration-dependent properties of substitutionally disordered
             systems. Typically, a CE model is built by fitting a few tens or
             hundreds of target quantities calculated by first-principles
             approaches. To validate the reliability of the model, a convergence
             test of the cross-validation (CV) score to the training set size is
             commonly conducted to verify the sufficiency of the training data.
             However, such a test only confirms the convergence of the predictive
             capability of the CE model within the training set, and it is unknown
             whether the convergence of the CV score would lead to robust
             thermodynamic simulation results such as order-disorder phase
             transition temperature Tc. In this work, using carbon defective MoC1-x
             as a model system and aided by the machine-learning force field
             technique, a training data pool with about 13000 configurations has
             been efficiently obtained and used to generate different training sets
             of the same size randomly. By conducting parallel Monte Carlo
             simulations with the CE models trained with different randomly
             selected training sets, the uncertainty in calculated Tc can be
             evaluated at different training set sizes. It is found that the
             training set size that is sufficient for the CV score to converge
             still leads to a significant uncertainty in the predicted Tc and that
             the latter can be considerably reduced by enlarging the training set
             to that of a few thousand configurations. This work highlights the
             importance of using a large training set to build the optimal CE model
             that can achieve robust statistical modeling results and the facility
             provided by the machine-learning force field approach to efficiently
             produce adequate training data.},
    doi =      {10.1021/acs.jctc.2c00017},
}

@Article{Liang_AdvcdTheoryandSims_2022_vNone_p2200206,
    author =   {Wenshuo Liang and Guimin Lu and Jianguo Yu},
    title =    {{Machine Learning Accelerates Molten Salt Simulations: Thermal
             Conductivity of MgCl             <sub>2</sub>             {-}NaCl
             Eutectic}},
    journal =  {Advcd Theory and Sims},
    year =     2022,
    pages =    2200206,
    doi =      {10.1002/adts.202200206},
}

@Article{Tang_ActaMaterialia_2022_v238_p118217,
    author =   {Hao Tang and Yin Zhang and Qing-Jie Li and Haowei Xu and Yuchi Wang
             and Yunzhi Wang and Ju Li},
    title =    {{High accuracy neural network interatomic potential for NiTi shape
             memory alloy}},
    journal =  {Acta Materialia},
    year =     2022,
    volume =   238,
    pages =    118217,
    doi =      {10.1016/j.actamat.2022.118217},
}

@Article{Piaggi_ProcNatlAcadSciUSA_2022_v119_pe2207294119,
    author =   {Pablo M Piaggi and Jack Weis and Athanassios Z Panagiotopoulos and
             Pablo G Debenedetti and Roberto Car},
    title =    {{Homogeneous ice nucleation in an ab initio machine-learning model of
             water}},
    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
    year =     2022,
    volume =   119,
    issue =    33,
    pages =    {e2207294119},
    annote =   {Molecular simulations have provided valuable insight into the
             microscopic mechanisms underlying homogeneous ice nucleation. While
             empirical models have been used extensively to study this phenomenon,
             simulations based on first-principles calculations have so far proven
             prohibitively expensive. Here, we circumvent this difficulty by using
             an efficient machine-learning model trained on density-functional
             theory energies and forces. We compute nucleation rates at atmospheric
             pressure, over a broad range of supercoolings, using the seeding
             technique and systems of up to hundreds of thousands of atoms
             simulated with ab initio accuracy. The key quantity provided by the
             seeding technique is the size of the critical cluster (i.e., a size
             such that the cluster has equal probabilities of growing or melting at
             the given supersaturation), which is used together with the equations
             of classical nucleation theory to compute nucleation rates. We find
             that nucleation rates for our model at moderate supercoolings are in
             good agreement with experimental measurements within the error of our
             calculation. We also study the impact of properties such as the
             thermodynamic driving force, interfacial free energy, and stacking
             disorder on the calculated rates.},
    PMCID =    {PMC9388152},
    doi =      {10.1073/pnas.2207294119},
}

@Article{Wang_JChemPhys_2022_v157_p024103,
    author =   {Feng Wang and Jun Cheng},
    title =    {{Automated workflow for computation of redox potentials, acidity
             constants, and solvation free energies accelerated by machine learning}},
    journal =  {J. Chem. Phys.},
    year =     2022,
    volume =   157,
    issue =    2,
    pages =    024103,
    annote =   {Fast evolution of modern society stimulates intense development of new
             materials with novel functionalities in energy and environmental
             applications. Due to rapid progress of computer science, computational
             design of materials with target properties has recently attracted a
             lot of interest. Accurate and efficient calculation of fundamental
             thermodynamic properties, including redox potentials, acidity
             constants, and solvation free energies, is of great importance for
             selection and design of desirable materials. Free energy calculation
             based on ab initio molecular dynamics (AIMD) can predict these
             properties with high accuracy at complex environments, however, they
             are being impeded by high computational costs. To address this issue,
             this work develops an automated scheme that combines iterative
             training of machine learning potentials (MLPs) and free energy
             calculation and demonstrates that these thermodynamic properties can
             be computed by ML accelerated MD with ab initio accuracy and a much
             longer time scale at cheaper costs, improving poor statistics and
             convergence of numerical integration by AIMD. Our automated scheme
             lays the foundation for computational chemistry-assisted materials
             design.},
    doi =      {10.1063/5.0098330},
}

@Article{Giese_JChemTheoryComput_2022_v18_p4304,
    author =   {Timothy J Giese and Jinzhe Zeng and Şölen Ekesan and Darrin
             M York},
    title =    {{Combined QM/MM, Machine Learning Path Integral Approach to Compute
             Free Energy Profiles and Kinetic Isotope Effects in RNA Cleavage
             Reactions}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume =   18,
    issue =    7,
    pages =    {4304--4317},
    annote =   {We present a fast, accurate, and robust approach for determination of
             free energy profiles and kinetic isotope effects for RNA
             2'-O-transphosphorylation reactions with inclusion of nuclear quantum
             effects. We apply a deep potential range correction (DPRc) for
             combined quantum mechanical/molecular mechanical (QM/MM) simulations
             of reactions in the condensed phase. The method uses the second-order
             density-functional tight-binding method (DFTB2) as a fast, approximate
             base QM model. The DPRc model modifies the DFTB2 QM interactions and
             applies short-range corrections to the QM/MM interactions to reproduce
             ab initio DFT (PBE0/6-31G*) QM/MM energies and forces. The DPRc thus
             enables both QM and QM/MM interactions to be tuned to high accuracy,
             and the QM/MM corrections are designed to smoothly vanish at a
             specified cutoff boundary (6 {\r{A}} in the present work). The
             computational speed-up afforded by the QM/MM+DPRc model enables free
             energy profiles to be calculated that include rigorous long-range
             QM/MM interactions under periodic boundary conditions and nuclear
             quantum effects through a path integral approach using a new interface
             between the AMBER and i-PI software. The approach is demonstrated
             through the calculation of free energy profiles of a native RNA
             cleavage model reaction and reactions involving thio-substitutions,
             which are important experimental probes of the mechanism. The
             DFTB2+DPRc QM/MM free energy surfaces agree very closely with the
             PBE0/6-31G* QM/MM results, and it is vastly superior to the DFTB2
             QM/MM surfaces with and without weighted thermodynamic perturbation
             corrections. 18O and 34S primary kinetic isotope effects are compared,
             and the influence of nuclear quantum effects on the free energy
             profiles is examined.},
    PMCID =    {PMC9283286},
    doi =      {10.1021/acs.jctc.2c00151},
}

@Article{Gupta_PhysRevB_2022_v106_p014311,
    author =   {Mayanak Kumar Gupta and Sajan Kumar and Ranjan Mittal and Samrath L.
             Chaplot},
    title =    {{Soft-phonon anharmonicity, floppy modes, and Na diffusion in
             Na3FY (Y=S,Se,Te): <i>Ab initio</i>  and machine-learned molecular dynamics
             simulations}},
    journal =  {Phys. Rev. B},
    year =     2022,
    volume =   106,
    issue =    1,
    pages =    014311,
    doi =      {10.1103/PhysRevB.106.014311},
}

@Article{Zhao_JPhysChemC_2022_v126_p10546,
    author =   {Wen Zhao and Hu Qiu and Wanlin Guo},
    title =    {{A Deep Neural Network Potential for Water Confined in Graphene
             Nanocapillaries}},
    journal =  {J. Phys. Chem. C},
    year =     2022,
    volume =   126,
    issue =    25,
    pages =    {10546--10553},
    doi =      {10.1021/acs.jpcc.2c02423},
}

@Article{Liu_JPhysChemA_2022_v126_p3926,
    author =   {Jinfeng Liu and Jinggang Lan and Xiao He},
    title =    {{Toward High-level Machine Learning Potential for Water Based on
             Quantum Fragmentation and Neural Networks}},
    journal =  {J. Phys. Chem. A},
    year =     2022,
    volume =   126,
    issue =    24,
    pages =    {3926--3936},
    annote =   {Accurate and efficient simulation of liquids, such as water and salt
             solutions, using high-level wave function theories is still a
             formidable task for computational chemists owing to the high
             computational costs. In this study, we develop a deep machine learning
             potential based on fragment-based second-order M{\o}ller-Plesset
             perturbation theory (DP-MP2) for water through neural networks. We
             show that the DP-MP2 potential predicts the structural, dynamical, and
             thermodynamic properties of liquid water in better agreement with the
             experimental data than previous studies based on density functional
             theory (DFT). The nuclear quantum effects (NQEs) on the properties of
             liquid water are also examined, which are noticeable in affecting the
             structural and dynamical properties of liquid water under ambient
             conditions. This work provides a general framework for quantitative
             predictions of the properties of condensed-phase systems with the
             accuracy of high-level wave function theory while achieving
             significant computational savings compared to ab initio simulations.},
    doi =      {10.1021/acs.jpca.2c00601},
}

@Article{Xu_ActaChimicaSinica_2022_v80_p598,
    author =   {Sen Xu and Liling Wu and Zhenyu Li},
    title =    {{Nucleation of Water Clusters in Gas Phase: A Computational Study Based
             on Neural Network Potential and Enhanced
             Sampling<sup>{\textreferencemark}</sup>}},
    journal =  {Acta Chimica Sinica},
    year =     2022,
    volume =   80,
    issue =    5,
    pages =    598,
    doi =      {10.6023/A22010003},
}

@Article{Lai_AdvMaterialsInter_2022_vNone_p2201346,
    author =   {Genming Lai and Junyu Jiao and Chi Fang and Ruiqi Zhang and Xianqi Xu
             and Liyuan Sheng and Yao Jiang and Chuying Ouyang and Jiaxin Zheng},
    title =    {{A Deep Neural Network Interface Potential for Li{-}Cu Systems}},
    journal =  {Adv Materials Inter},
    year =     2022,
    pages =    2201346,
    doi =      {10.1002/admi.202201346},
}

@Article{Devergne_JChemTheoryComput_2022_vNone_pNone,
    author =   {Timoth{\'e}e Devergne and Th{\'e}o Magrino and Fabio Pietrucci and A
             Marco Saitta},
    title =    {{Combining Machine Learning Approaches and Accurate <i>Ab Initio</i>
             Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    annote =   {The study of the thermodynamics, kinetics, and microscopic mechanisms
             of chemical reactions in solution requires the use of advanced free-
             energy methods for predictions to be quantitative. This task is
             however a formidable one for atomistic simulation methods, as the cost
             of quantum-based ab initio approaches, to obtain statistically
             meaningful samplings of the relevant chemical spaces and networks,
             becomes exceedingly heavy. In this work, we critically assess the
             optimal structure and minimal size of an ab initio training set able
             to lead to accurate free-energy profiles sampled with neural network
             potentials. The results allow one to propose an ab initio protocol
             where the ad hoc inclusion of a machine-learning (ML)-based task can
             significantly increase the computational efficiency, while keeping the
             ab initio accuracy and, at the same time, avoiding some of the
             notorious extrapolation risks in typical atomistic ML approaches. We
             focus on two representative, and computationally challenging, reaction
             steps of the classic Strecker-cyanohydrin mechanism for glycine
             synthesis in water solution, where the main precursors are
             formaldehyde and hydrogen cyanide. We demonstrate that
             indistinguishable ab initio quality results are obtained, thanks to
             the ML subprotocol, at about 1 order of magnitude less of
             computational load.},
    doi =      {10.1021/acs.jctc.2c00400},
}

@Article{Lu_JChemTheoryComput_2022_vNone_pNone,
    author =   {Denghui Lu and Wanrun Jiang and Yixiao Chen and Linfeng Zhang and
             Weile Jia and Han Wang and Mohan Chen},
    title =    {{DP Compress: A Model Compression Scheme for Generating Efficient Deep
             Potential Models}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume=18,
    issue=9,
    pages={5555--5567},
    annote =   {Machine-learning-based interatomic potential energy surface (PES)
             models are revolutionizing the field of molecular modeling. However,
             although much faster than electronic structure schemes, these models
             suffer from costly computations via deep neural networks to predict
             the energy and atomic forces, resulting in lower running efficiency as
             compared to the typical empirical force fields. Herein, we report a
             model compression scheme for boosting the performance of the Deep
             Potential (DP) model, a deep learning-based PES model. This scheme, we
             call DP Compress, is an efficient postprocessing step after the
             training of DP models (DP Train). DP Compress combines several DP-
             specific compression techniques, which typically speed up DP-based
             molecular dynamics simulations by an order of magnitude faster and
             consume an order of magnitude less memory. We demonstrate that DP
             Compress is sufficiently accurate by testing a variety of physical
             properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to
             both CPU and GPU machines and is publicly available online.},
    doi =      {10.1021/acs.jctc.2c00102},
}

@Article{Zhang_JournalofMolecularLiquids_2022_v367_p120500,
    author =   {Wei Zhang and Li Zhou and Bin Yang and Tinggui Yan},
    title =    {{Molecular dynamics simulations of LiCl ion pairs in high temperature
             aqueous solutions by deep learning potential}},
    journal =  {Journal of Molecular Liquids},
    year =     2022,
    volume =   367,
    pages =    120500,
    doi =      {10.1016/j.molliq.2022.120500},
}

@Article{Wang_NuclFusion_2022_v62_p126013,
    author =   {Xiaoyang Wang and Yinan Wang and Linfeng Zhang and Fuzhi Dai and Han
             Wang},
    title =    {{A tungsten deep neural-network potential for simulating mechanical
             property degradation under fusion service environment}},
    journal =  {Nucl. Fusion},
    year =     2022,
    volume =   62,
    issue =    12,
    pages =    126013,
    annote =   {<jats:title>Abstract</jats:title>                <jats:p>Tungsten is a
             promising candidate material in fusion energy facilities. Molecular
             dynamics (MD) simulations reveal the atomistic scale mechanisms, so
             they are crucial for the understanding of the macroscopic property
             deterioration of tungsten under harsh and complex service
             environments. The interatomic potential used in the MD simulations is
             required to accurately describe a wide spectrum of relevant defect
             properties, which is by far challenging to the existing interatomic
             potentials. In this paper, we propose a new three-body embedding
             descriptor and hybridize it into the deep-potential (DP) framework, an
             end-to-end deep learning interatomic potential model. The potential
             model for tungsten, named DP-HYB, is trained with a database
             constructed by the concurrent learning method. The DP-HYB model is
             able to accurately predict elastic constants, stacking fault energy,
             the formation energies of free surfaces, and point defects, which are
             considered in the training dataset. It also accurately evaluates the
             formation energies of grain boundaries and prismatic loops, the core
             structure of screw dislocation, the Peierls barrier, and the
             transition path of the screw dislocation migration, which do not
             explicitly present in the training dataset. The DP-HYB is a good
             candidate for the atomistic simulations of tungsten property
             deterioration, especially those involving the mechanical property
             degradation under the harsh fusion service environment.</jats:p>},
    doi =      {10.1088/1741-4326/ac888b},
}

@Article{Uporov_Intermetallics_2022_v151_p107678,
    author =   {S.A. Uporov and S. Kh Estemirova and E.V. Sterkhov and I.A. Balyakin
             and A.A. Rempel},
    title =    {{Magnetocaloric effect in ScGdTbDyHo high-entropy alloy: Impact of
             synthesis route}},
    journal =  {Intermetallics},
    year =     2022,
    volume =   151,
    pages =    107678,
    doi =      {10.1016/j.intermet.2022.107678},
}

@Article{Fan_JChemPhys_2022_v157_p114801,
    author =   {Zheyong Fan and Yanzhou Wang and Penghua Ying and Keke Song and Junjie
             Wang and Yong Wang and Zezhu Zeng and Ke Xu and Eric Lindgren and J
             Magnus Rahm and Alexander J Gabourie and Jiahui Liu and Haikuan Dong
             and Jianyang Wu and Yue Chen and Zheng Zhong and Jian Sun and Paul
             Erhart and Yanjing Su and Tapio Ala-Nissila},
    title =    {{GPUMD: A package for constructing accurate machine-learned potentials
             and performing highly efficient atomistic simulations}},
    journal =  {J. Chem. Phys.},
    year =     2022,
    volume =   157,
    issue =    11,
    pages =    114801,
    annote =   {We present our latest advancements of machine-learned potentials
             (MLPs) based on the neuroevolution potential (NEP) framework
             introduced in Fan et al. [Phys. Rev. B 104, 104309 (2021)] and their
             implementation in the open-source package gpumd. We increase the
             accuracy of NEP models both by improving the radial functions in the
             atomic-environment descriptor using a linear combination of Chebyshev
             basis functions and by extending the angular descriptor with some
             four-body and five-body contributions as in the atomic cluster
             expansion approach. We also detail our efficient implementation of the
             NEP approach in graphics processing units as well as our workflow for
             the construction of NEP models and demonstrate their application in
             large-scale atomistic simulations. By comparing to state-of-the-art
             MLPs, we show that the NEP approach not only achieves above-average
             accuracy but also is far more computationally efficient. These results
             demonstrate that the gpumd package is a promising tool for solving
             challenging problems requiring highly accurate, large-scale atomistic
             simulations. To enable the construction of MLPs using a minimal
             training set, we propose an active-learning scheme based on the latent
             space of a pre-trained NEP model. Finally, we introduce three separate
             Python packages, viz., gpyumd, calorine, and pynep, that enable the
             integration of gpumd into Python workflows.},
    doi =      {10.1063/5.0106617},
}

@Article{Devergne_JChemTheoryComput_2022_v18_p5410,
    author =   {Timoth{\'e}e Devergne and Th{\'e}o Magrino and Fabio Pietrucci and A
             Marco Saitta},
    title =    {{Combining Machine Learning Approaches and Accurate <i>Ab Initio</i>
             Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume =   18,
    issue =    9,
    pages =    {5410--5421},
    annote =   {The study of the thermodynamics, kinetics, and microscopic mechanisms
             of chemical reactions in solution requires the use of advanced free-
             energy methods for predictions to be quantitative. This task is
             however a formidable one for atomistic simulation methods, as the cost
             of quantum-based ab initio approaches, to obtain statistically
             meaningful samplings of the relevant chemical spaces and networks,
             becomes exceedingly heavy. In this work, we critically assess the
             optimal structure and minimal size of an ab initio training set able
             to lead to accurate free-energy profiles sampled with neural network
             potentials. The results allow one to propose an ab initio protocol
             where the ad hoc inclusion of a machine-learning (ML)-based task can
             significantly increase the computational efficiency, while keeping the
             ab initio accuracy and, at the same time, avoiding some of the
             notorious extrapolation risks in typical atomistic ML approaches. We
             focus on two representative, and computationally challenging, reaction
             steps of the classic Strecker-cyanohydrin mechanism for glycine
             synthesis in water solution, where the main precursors are
             formaldehyde and hydrogen cyanide. We demonstrate that
             indistinguishable ab initio quality results are obtained, thanks to
             the ML subprotocol, at about 1 order of magnitude less of
             computational load.},
    doi =      {10.1021/acs.jctc.2c00400},
}

@Article{Zhang_PhysRevB_2022_v106_p094107,
    author =   {Zhipeng Zhang and Liuqing Chen and Junyi Guo and Xianyin Duan and Bin
             Shan and Xianbao Duan},
    title =    {{Strategy to consider element distribution when constructing training
             datasets for developing machine learning potentials of alloys based on
             a Monte-Carlo-like method}},
    journal =  {Phys. Rev. B},
    year =     2022,
    volume =   106,
    issue =    9,
    pages =    094107,
    doi =      {10.1103/PhysRevB.106.094107},
}

@Article{Lai_AdvMaterialsInter_2022_v9_p2201346,
    author =   {Genming Lai and Junyu Jiao and Chi Fang and Ruiqi Zhang and Xianqi Xu
             and Liyuan Sheng and Yao Jiang and Chuying Ouyang and Jiaxin Zheng},
    title =    {{A Deep Neural Network Interface Potential for Li{-}Cu Systems}},
    journal =  {Adv Materials Inter},
    year =     2022,
    volume =   9,
    issue =    27,
    pages =    2201346,
    doi =      {10.1002/admi.202201346},
}

@Article{Wang_JChemPhys_2022_v157_p074304,
    author =   {Xiaoning Wang and Haidi Wang and Qiquan Luo and Jinlong Yang},
    title =    {{Structural and electrocatalytic properties of copper clusters: A study
             via deep learning and first principles}},
    journal =  {J. Chem. Phys.},
    year =     2022,
    volume =   157,
    issue =    7,
    pages =    074304,
    annote =   {Determining the atomic structure of clusters has been a long-term
             challenge in theoretical calculations due to the high computational
             cost of density-functional theory (DFT). Deep learning potential (DP),
             as an alternative way, has been demonstrated to be able to conduct
             cluster simulations with close-to DFT accuracy but at a much lower
             computational cost. In this work, we update 34 structures of the 41 Cu
             clusters with atomic numbers ranging from 10 to 50 by combining global
             optimization and the DP model. The calculations show that the
             configuration of small Cun clusters (n = 10-15) tends to be oblate and
             it gradually transforms into a cage-like configuration as the size
             increases (n > 15). Based on the updated structures, their relative
             stability and electronic properties are extensively studied. In
             addition, we select three different clusters (Cu13, Cu38, and Cu49) to
             study their electrocatalytic ability of CO2 reduction. The simulation
             indicates that the main product is CO for these three clusters, while
             the selectivity of hydrocarbons is inhibited. This work is expected to
             clarify the ground-state structures and fundamental properties of Cun
             clusters, and to guide experiments for the design of Cu-based
             catalysts.},
    doi =      {10.1063/5.0100505},
}

@Article{Li_AIPAdvances_2022_v12_p085111,
    author =   {Wentao Li and Chenxiu Yang},
    title =    {{Thermal transport properties of monolayer GeS and SnS: A comparative
             study based on machine learning and SW interatomic potential models}},
    journal =  {AIP Advances},
    year =     2022,
    volume =   12,
    issue =    8,
    pages =    085111,
    annote =   {<jats:p> Phonon transport properties of two-dimensional materials can
             play a crucial role in the thermal management of low-dimensional
             electronic devices and thermoelectric applications. In this study,
             both the empirical Stillinger{\textendash}Weber (SW) and machine
             learning interatomic potentials are employed to investigate the
             lattice thermal conductivity of monolayer GeS and SnS through solving
             the phonon Boltzmann transport equation. The accuracy of the two types
             of interatomic potentials and their performance for the evaluation of
             thermal conductivity are verified by analyzing phonon harmonic and
             anharmonic properties. Our results indicate that the thermal
             conductivity can be predicted more accurately with a machine learning
             approach, while the SW potential gives rise to an overestimated value
             for both monolayers. In addition, the in-plane anisotropy of thermal
             transport properties existing in these monolayers can be confirmed by
             both potential models. Moreover, the origins of the deviation existing
             in calculated thermal conductivities, including both the effects of
             interatomic potential models and monolayer compositions, are
             elucidated through uncovering the underlying phonon transport
             mechanisms. This study highlights that in contrast to the machine
             learning approach, more careful verification is required for the
             simulation of thermal transport properties when empirical interatomic
             potential models are employed. </jats:p>},
    doi =      {10.1063/5.0099448},
}


@Article{Du_PhysChemChemPhys_2022_v24_p18361,
    author =   {Yunzhen Du and Zhaocang Meng and Qiang Yan and Canglong Wang and Yuan
             Tian and Wenshan Duan and Sheng Zhang and Ping Lin},
    title =    {{Deep potential for a face-centered cubic Cu system at finite
             temperatures}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2022,
    volume =   24,
    issue =    30,
    pages =    {18361--18369},
    annote =   {The state-of-the-art method generating potential functions used in
             molecular dynamics is based on machine learning with neural networks,
             which is critical for molecular dynamics simulation. This method
             provides an efficient way for fitting multi-variable nonlinear
             functions, attracting extensive attention in recent years. Generally,
             the quality of potentials fitted by neural networks is heavily
             affected by training datasets and the training process and could be
             ensured by comprehensively verificating the model accuracy. In this
             study, we obtained the neural network potential of face-centered cubic
             (FCC) Cu with the most accurate and adequate training datasets from
             first-principle calculations and the training process performed by
             Deep Potential Molecular Dynamics (DeePMD). This potential could not
             only succeed in reproductions of the variety of properties of Cu at 0
             K, but also have a good performance at finite temperatures, such as
             predicting elastic constants and the melting point. Moreover, our
             potential has a better generalization capacity to predict the grain
             boundary energy without including extra datasets about grain boundary
             structures. These results support the applicability of the method
             under more practical conditions.},
    doi =      {10.1039/D2CP02758E},
}
@Article{Ye_FaradayDiscuss_2022_v236_p352,
    author =   {Zifan Ye and Cunzhi Zhang and Giulia Galli},
    title =    {{Photoelectron spectra of water and simple aqueous solutions at extreme
             conditions}},
    journal =  {Faraday Discuss.},
    year =     2022,
    volume =   236,
    issue =    0,
    pages =    {352--363},
    annote =   {Determining the electronic structure of aqueous solutions at extreme
             conditions is an important step towards understanding chemical bonding
             and reactions in water under pressure (P) and at high temperature (T).
             We present calculations of the photoelectron spectra of water and a
             simple solution of NaCl under pressure at conditions relevant to the
             Earth's interior (11 GPa and 1000 K). We combine first-principles and
             deep-potential molecular dynamics with electronic structure
             calculations with dielectric-dependent hybrid functionals. These
             functionals are defined with a fraction of exact exchange determined
             from the dielectric constant of the liquid computed in extreme
             conditions. We find a broadening of the spectra relative to ambient
             conditions, particularly prominent in the merging of the two main
             peaks below the onset of the spectra. Furthermore we find an overall
             red shift at high pressure and temperature, which is however not
             constant over the whole energy range and varies between 1.1 and 2.4
             eV. Our results also show that the anion energy levels are closer to
             the valence band maximum of the liquid than at ambient conditions,
             indicating that as P and T are increased, the defect levels of Cl- and
             OH- in water may eventually lie below the valence band maximum of
             water. Finally, we characterize the ionization potential of hydrated
             species deriving from rapid water dissociation, e.g. hydrated
             hydroxide and hydronium, and we elucidate the electronic states
             associated with proton transfer events at high pressure. Our results
             represent a first, important step in predicting the electronic
             properties of solutions in super-critical conditions.},
    doi =      {10.1039/d2fd00003b},
}

@Article{Raman_JPhysChemA_2022_v126_p7283,
    author =   {Abhinav S Raman and Annabella Selloni},
    title =    {{Modeling the Solvation and Acidity of Carboxylic Acids Using an <i>Ab
             Initio</i> Deep Neural Network Potential}},
    journal =  {J. Phys. Chem. A},
    year =     2022,
    volume =   126,
    issue =    40,
    pages =    {7283--7290},
    annote =   {Formic and acetic acid constitute the simplest of carboxylic acids,
             yet they exhibit fascinating chemistry in the condensed phase such as
             proton transfer and dimerization. The go-to method of choice for
             modeling these rare events have been accurate but expensive ab initio
             molecular dynamics simulations. In this study, we present a deep
             neural network potential trained using accurate ab initio data that
             can be used in tandem with enhanced-sampling methods to perform an
             efficient exploration of the free-energy surface of aqueous solutions
             of weak carboxylic acids. In particular, we show that our model
             captures proton dissociation and provides a good estimate of the pKa,
             as well as the dimerization of formic and acetic acid. This provides a
             suitable starting point for applications in different research areas
             where computational efficiency coupled with the accuracy of ab initio
             methods is required.},
    doi =      {10.1021/acs.jpca.2c06252},
}

@Article{Liu_NanoLett_2022_v22_p7791,
    author =   {Xiaoqian Liu and Ran Peng and Zhaoru Sun and Jianpeng Liu},
    title =    {{Moir{\'e} Phonons in Magic-Angle Twisted Bilayer Graphene}},
    journal =  {Nano Lett.},
    year =     2022,
    volume =   22,
    issue =    19,
    pages =    {7791--7797},
    annote =   {Magic-angle twisted bilayer graphene (TBG) has attracted significant
             interest recently due to the discoveries of diverse correlated and
             topological states. In this work, we study the phonon properties in
             magic-angle TBG based on many-body classical potential and interatomic
             forces generated by a deep neural network trained with data from ab
             initio calculations. We have discovered a number of soft modes which
             can exhibit dipolar, quadrupolar, and octupolar vibrational patterns
             in real space, as well as some time-reversal breaking chiral phonon
             modes. We have further studied the phonon effects on the electronic
             structures by freezing certain soft phonon modes. We find that if a
             soft quadrupolar phonon mode is assumed to be frozen, the system would
             exhibit a charge order which is perfectly consistent with recent
             experiments. Moreover, once some low-frequency C2z-breaking modes get
             frozen, the Dirac points at the charge neutrality point would be
             gapped out, which provides an alternative perspective to the origin of
             correlated insulator state at charge neutrality point.},
    PMCID =    {PMC9562463},
    doi =      {10.1021/acs.nanolett.2c02010},
}

@Article{Loose_JChemTheoryComput_2022_v18_p5856,
    author =   {Timothy D Loose and Patrick G Sahrmann and Gregory A Voth},
    title =    {{Centroid Molecular Dynamics Can Be Greatly Accelerated through Neural
             Network Learned Centroid Forces Derived from Path Integral Molecular
             Dynamics}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume =   18,
    issue =    10,
    pages =    {5856--5863},
    annote =   {For nearly the past 30 years, centroid molecular dynamics (CMD) has
             proven to be a viable classical-like phase space formulation for the
             calculation of quantum dynamical properties. However, calculation of
             the centroid effective force remains a significant computational cost
             and limits the ability of CMD to be an efficient approach to study
             condensed phase quantum dynamics. In this paper, we introduce a neural
             network-based methodology for first learning the centroid effective
             force from path integral molecular dynamics data, which is
             subsequently used as an effective force field to evolve the centroids
             directly with the CMD algorithm. This method, called machine-learned
             centroid molecular dynamics (ML-CMD), is faster and far less costly
             than both standard "on the fly" CMD and ring polymer molecular
             dynamics (RPMD). The training aspect of ML-CMD is also
             straightforwardly implemented utilizing the DeepMD software kit. ML-
             CMD is then applied to two model systems to illustrate the approach:
             liquid para-hydrogen and water. The results show comparable accuracy
             to both CMD and RPMD in the estimation of quantum dynamical
             properties, including the self-diffusion constant and velocity time
             correlation function, but with significantly reduced overall
             computational cost.},
    PMCID =    {PMC9558744},
    doi =      {10.1021/acs.jctc.2c00706},
}

@Article{Zhai_ComputationalMaterialsScience_2023_v216_p111843,
    author =   {B. Zhai and H.P. Wang},
    title =    {{Accurate interatomic potential for the nucleation in liquid Ti-Al
             binary alloy developed by deep neural network learning method}},
    journal =  {Computational Materials Science},
    year =     2023,
    volume =   216,
    pages =    111843,
    doi =      {10.1016/j.commatsci.2022.111843},
}

@Article{Han_BriefBioinform_2021_v22_pNone,
    author =   {Yanqiang Han and Zhilong Wang and Zhiyun Wei and Jinyun Liu and Jinjin
             Li},
    title =    {{Machine learning builds full-QM precision protein force fields in
             seconds}},
    journal =  {Brief. Bioinform.},
    year =     2021,
    volume =   22,
    issue =    6,
    annote =   {Full-quantum mechanics (QM) calculations are extraordinarily precise
             but difficult to apply to large systems, such as biomolecules.
             Motivated by the massive demand for efficient calculations for large
             systems at the full-QM level and by the significant advances in
             machine learning, we have designed a neural network-based two-body
             molecular fractionation with conjugate caps (NN-TMFCC) approach to
             accelerate the energy and atomic force calculations of proteins. The
             results show very high precision for the proposed NN potential energy
             surface models of residue-based fragments, with energy root-mean-
             squared errors (RMSEs) less than 1.0{~}kcal/mol and force RMSEs less
             than 1.3{~}kcal/mol/{\r{A}} for both training and testing sets. The
             proposed NN-TMFCC method calculates the energies and atomic forces of
             15 representative proteins with full-QM precision in 10-100{~}s, which
             is thousands of times faster than the full-QM calculations. The
             computational complexity of the NN-TMFCC method is independent of the
             protein size and only depends on the number of residue species, which
             makes this method particularly suitable for rapid prediction of large
             systems with tens of thousands or even hundreds of thousands of times
             acceleration. This highly precise and efficient NN-TMFCC approach
             exhibits considerable potential for performing energy and force
             calculations, structure predictions and molecular dynamics simulations
             of proteins with full-QM precision.},
    doi =      {10.1093/bib/bbab158},
}

@Article{Li_JPhysChemA_2022_v126_p9154,
    author =   {Wenfei Li and Qi Ou and Yixiao Chen and Yu Cao and Renxi Liu and
             Chunyi Zhang and Daye Zheng and Chun Cai and Xifan Wu and Han Wang and
             Mohan Chen and Linfeng Zhang},
    title =    {{DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical
             Models and Machine Learning Potentials}},
    journal =  {J. Phys. Chem. A},
    year =     2022,
    volume =   126,
    issue =    49,
    pages =    {9154--9164},
    annote =   {Recently, the development of machine learning (ML) potentials has made
             it possible to perform large-scale and long-time molecular simulations
             with the accuracy of quantum mechanical (QM) models. However, for
             different levels of QM methods, such as density functional theory
             (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte
             Carlo, etc., generating a sufficient amount of data for training an ML
             potential has remained computationally challenging due to their high
             cost. In this work, we demonstrate that this issue can be largely
             alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS
             employs a computationally efficient neural network-based functional
             model to construct a correction term added upon a cheap DFT model.
             Upon training, DeePKS offers closely matched energies and forces
             compared with high-level QM method, but the number of training data
             required is orders of magnitude less than that required for training a
             reliable ML potential. As such, DeePKS can serve as a bridge between
             expensive QM models and ML potentials: one can generate a decent
             amount of high-accuracy QM data to train a DeePKS model and then use
             the DeePKS model to label a much larger amount of configurations to
             train an ML potential. This scheme for periodic systems is implemented
             in a DFT package ABACUS, which is open source and ready for use in
             various applications.},
    doi =      {10.1021/acs.jpca.2c05000},
}

@Article{Wang_GeophysicalResearchLetters_2022_v49_pNone,
    author =   {Dong Wang and Zhongqing Wu and Xin Deng},
    title =    {{Thermal Conductivity of Hydrous Wadsleyite Determined by
             Non{-}Equilibrium Molecular Dynamics Based on Machine Learning}},
    journal =  {Geophysical Research Letters},
    year =     2022,
    volume =   49,
    issue =    22,
    doi =      {10.1029/2022GL100337},
}

@Article{Giese_JPhysChemA_2022_v126_p8519,
    author =   {Timothy J Giese and Jinzhe Zeng and Darrin M York},
    title =    {{Multireference Generalization of the Weighted Thermodynamic
             Perturbation Method}},
    journal =  {J. Phys. Chem. A},
    year =     2022,
    volume =   126,
    issue =    45,
    pages =    {8519--8533},
    annote =   {We describe the generalized weighted thermodynamic perturbation (gwTP)
             method for estimating the free energy surface of an expensive "high-
             level" potential energy function from the umbrella sampling performed
             with multiple inexpensive "low-level" reference potentials. The gwTP
             method is a generalization of the weighted thermodynamic perturbation
             (wTP) method developed by Li and co-workers [J. Chem. Theory Comput.
             2018, 14, 5583-5596] that uses a single "low-level" reference
             potential. The gwTP method offers new possibilities in model design
             whereby the sampling generated from several low-level potentials may
             be combined (e.g., specific reaction parameter models that might have
             variable accuracy at different stages of a multistep reaction). The
             gwTP method is especially well suited for use with machine learning
             potentials (MLPs) that are trained against computationally expensive
             ab initio quantum mechanical/molecular mechanical (QM/MM) energies and
             forces using active learning procedures that naturally produce
             multiple distinct neural network potentials. Simulations can be
             performed with greater sampling using the fast MLPs and then corrected
             to the ab initio level using gwTP. The capabilities of the gwTP method
             are demonstrated by creating reference potentials based on the MNDO/d
             and DFTB2/MIO semiempirical models supplemented with the "range-
             corrected deep potential" (DPRc). The DPRc parameters are trained to
             ab initio QM/MM data, and the potentials are used to calculate the
             free energy surface of stepwise mechanisms for nonenzymatic RNA
             2'-O-transesterification model reactions. The extended sampling made
             possible by the reference potentials allows one to identify
             unequilibrated portions of the simulations that are not always evident
             from the short time scale commonly used with ab initio QM/MM
             potentials. We show that the reference potential approach can yield
             more accurate ab initio free energy predictions than the wTP method or
             what can be reasonably afforded from explicit ab initio QM/MM
             sampling.},
    PMCID =    {PMC9771595},
    doi =      {10.1021/acs.jpca.2c06201},
}

@Article{Liu_JAmChemSoc_2022_v144_p20601,
    author =   {Jin-Cheng Liu and Langli Luo and Hai Xiao and Junfa Zhu and Yang He
             and Jun Li},
    title =    {{Metal Affinity of Support Dictates Sintering of Gold Catalysts}},
    journal =  {J. Am. Chem. Soc.},
    year =     2022,
    volume =   144,
    issue =    45,
    pages =    {20601--20609},
    annote =   {Sintering during heterogeneous catalytic reactions is one of the most
             notorious deactivation channels in catalysts of supported metal
             nanoparticles. It is therefore critical to understand the effect of
             support on the sintering behavior. Here, by using in situ aberration-
             corrected transmission electron microscopy and computational modeling,
             the atomic-scale dynamic interactions are revealed between Au
             nanoparticles and various supports. It is found that Au nanoparticles
             on ceria have a smaller contact angle and are apparently less mobile,
             especially at surface steps when compared with those on the amorphous
             silica. Analogous to hydrophilicity, we attribute the origin of
             mobility of small nanoparticles to metal affinity, which determines
             the interaction between metal and support material. Ab initio
             molecular dynamics (AIMD) and machine learning-based deep potential
             molecular dynamics (DPMD) simulations directly capture a coalescence
             process on the silica surface and the strong pinning of gold on ceria.
             The joint experimental and theoretical results on the atomic scale
             demonstrate the metal affinity of active and inert supports as the key
             descriptor pertinent to sintering and deactivation of heterogeneous
             catalysts.},
    doi =      {10.1021/jacs.2c06785},
}

@Article{Matusalem_ProcNatlAcadSciUSA_2022_v119_pe2203397119,
    author =   {Filipe Matusalem and J{\'e}ssica {Santos Rego} and Maurice {de Koning}},
    title =    {{Plastic deformation of superionic water ices}},
    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
    year =     2022,
    volume =   119,
    issue =    45,
    pages =    {e2203397119},
    annote =   {Due to their potential role in the peculiar geophysical properties of
             the ice giants Neptune and Uranus, there has been a growing interest
             in superionic (SI) phases of water ice. So far, however, little
             attention has been given to their mechanical properties, even though
             plastic deformation processes in the interiors of planets are known to
             affect long-term processes, such as plate tectonics and mantle
             convection. Here, using density functional theory calculations and
             machine learning techniques, we assess the mechanical response of
             high-pressure/temperature solid phases of water in terms of their
             ideal shear strength (ISS) and dislocation behavior. The ISS results
             are well described by the renormalized Frenkel model of ideal strength
             and indicate that the SI ices are expected to be highly ductile. This
             is further supported by deep neural network molecular dynamics
             simulations for the behavior of lattice dislocations for the SI face-
             centered cubic (fcc) phase. Dislocation velocity data indicate
             effective shear viscosities that are orders of magnitude smaller than
             that of Earth's lower mantle, suggesting that the plastic flow of the
             internal icy layers in Neptune and Uranus may be significantly faster
             than previously foreseen.},
    PMCID =    {PMC9659364},
    doi =      {10.1073/pnas.2203397119},
}

@Article{Li_JChemPhys_2022_v157_p174303,
    author =   {Gen Li and Bing-Zhong Hu and Wen-Hao Mao and Nuo Yang and Jing-Tao
             L{\"u}},
    title =    {{Order of magnitude reduction in Joule heating of single molecular
             junctions between graphene electrodes}},
    journal =  {J. Chem. Phys.},
    year =     2022,
    volume =   157,
    issue =    17,
    pages =    174303,
    annote =   {Maintaining stability of single-molecular junctions (SMJs) in the
             presence of current flow is a prerequisite for their potential device
             applications. However, theoretical understanding of nonequilibrium
             heat transport in current-carrying SMJs is a challenging problem due
             to the different kinds of nonlinear interactions involved, including
             electron-vibration and anharmonic vibrational coupling. Here, we
             overcome this challenge by accelerating Langevin-type current-induced
             molecular dynamics using machine-learning potential derived from
             density functional theory. We show that SMJs with graphene electrodes
             generate an order of magnitude less heating than those with gold
             electrodes. This is rooted in the better phonon spectral overlap of
             graphene with molecular vibrations, rendering harmonic phonon heat
             transport being dominant. In contrast, in a spectrally mismatched
             junction with gold electrodes, anharmonic coupling becomes important
             to transport heat away from the molecule to surrounding electrodes.
             Our work paves the way for studying current-induced heat transport and
             energy redistribution in realistic SMJs.},
    doi =      {10.1063/5.0118952},
}

@Article{Wang_PhysRevMaterials_2022_v6_p113603,
    author =   {Rui Wang and Xiaoxiao Ma and Linfeng Zhang and Han Wang and David J.
             Srolovitz and Tongqi Wen and Zhaoxuan Wu},
    title =    {{Classical and machine learning interatomic potentials for BCC vanadium}},
    journal =  {Phys. Rev. Materials},
    year =     2022,
    volume =   6,
    issue =    11,
    pages =    113603,
    doi =      {10.1103/PhysRevMaterials.6.113603},
}

@Article{McMichael_PhysRevAppl_2022_v18_p054066,
    author =   {Robert D McMichael and Sean M Blakley},
    title =    {{Piezo- and Pyroelectricity in Zirconia: A Study with Machine-Learned
             Force Fields}},
    journal =  {Phys. Rev. Appl.},
    year =     2022,
    volume =   18,
    issue =    5,
    pages =    054066,
    annote =   {Measurements to estimate parameters of a model are commonplace in the
             physical sciences, where the traditional approach to automation is to
             use a sequence of preselected settings followed by least-squares
             fitting of a model function to the data. This measure-then-fit
             approach is simple and effective and entirely appropriate for many
             applications but when measurement resources are limited, efficiency
             becomes more important. To increase efficiency, Bayesian experiment
             design allows measurement settings to be chosen adaptively based on
             accumulated data and utility, the predicted improvement in results as
             a function of settings. However, the calculation of utility has been
             judged too impractical for most applications. In this paper, we
             introduce computational methods and simplified algorithms that
             accelerate utility calculations by over an order of magnitude, with
             only slight degradation in measurement efficiency. The methods
             eliminate utility calculation as a barrier to practical application of
             efficient adaptive measurement.},
    PMCID =    {PMC9791979},
    doi =      {10.1103/PhysRevApplied.18.054066},
}

@Article{McMichael_PhysRevAppl_2022_v18_p054022,
    author =   {Robert D McMichael and Sean M Blakley},
    title =    {{Phonon Thermal Transport in  Bi2Te3
             from a Deep-Neural-Network Interatomic Potential}},
    journal =  {Phys. Rev. Appl.},
    year =     2022,
    volume =   18,
    issue =    5,
    pages =    054022,
    annote =   {Measurements to estimate parameters of a model are commonplace in the
             physical sciences, where the traditional approach to automation is to
             use a sequence of preselected settings followed by least-squares
             fitting of a model function to the data. This measure-then-fit
             approach is simple and effective and entirely appropriate for many
             applications but when measurement resources are limited, efficiency
             becomes more important. To increase efficiency, Bayesian experiment
             design allows measurement settings to be chosen adaptively based on
             accumulated data and utility, the predicted improvement in results as
             a function of settings. However, the calculation of utility has been
             judged too impractical for most applications. In this paper, we
             introduce computational methods and simplified algorithms that
             accelerate utility calculations by over an order of magnitude, with
             only slight degradation in measurement efficiency. The methods
             eliminate utility calculation as a barrier to practical application of
             efficient adaptive measurement.},
    PMCID =    {PMC9791979},
    doi =      {10.1103/PhysRevApplied.18.054022},
}

@Article{He_PhysRevB_2022_v106_p174101,
    author =   {Ri He and Hongyu Wu and Yi Lu and Zhicheng Zhong},
    title =    {{Origin of negative thermal expansion and pressure-induced
             amorphization in zirconium tungstate from a machine-learning potential}},
    journal =  {Phys. Rev. B},
    year =     2022,
    volume =   106,
    issue =    17,
    pages =    174101,
    doi =      {10.1103/PhysRevB.106.174101},
}

@Article{Wan_NewJPhys_2022_v24_p113012,
    author =   {Zhongyu Wan and Chao Zhang and Tianyi Yang and Wenjun Xu and Ruiqin
             Zhang},
    title =    {{Predicted superconductivity and superionic state in the electride
             Li<sub>5</sub>N under high pressure}},
    journal =  {New J. Phys.},
    year =     2022,
    volume =   24,
    issue =    11,
    pages =    113012,
    annote =   {<jats:title>Abstract</jats:title>                <jats:p>Recently,
             electrides have received increasing attention due to their
             multifunctional properties as superconducting, catalytic, insulating,
             and electrode materials, with potential to offer other performance and
             possess novel physical states. This work uncovers that
             Li<jats:sub>5</jats:sub>N as an electride possess four novel physical
             states simultaneously: electride state, super-coordinated state,
             superconducting state, and superionic state. By obtaining high-
             pressure phase diagrams of the Li{\textendash}N system at
             150{\textendash}350{~}GPa using a crystal structure search algorithm,
             we find that Li<jats:sub>5</jats:sub>N can remain stable as
             <jats:italic>P</jats:italic>6/<jats:italic>mmm</jats:italic> structure
             and has a 14-fold super-coordination number, as verified by Bader
             charge and electron localization function analysis. Its
             superconducting transition temperature reaches the highest at
             150{~}GPa (<jats:italic>T</jats:italic>
             <jats:sub>c</jats:sub> = 48.97{~}K). Besides,
             Li<jats:sub>5</jats:sub>N exhibits the superionic state at 3000{~}K,
             in which N atoms act like solid, while some Li atoms flow like liquid.
             The above results are further verified at a macroscopic level by using
             deep learning potential molecular dynamics simulations.</jats:p>},
    doi =      {10.1088/1367-2630/ac9cff},
}

@Article{Fronzi_Nanomaterials_2022_v12_p3891,
    author =   {Marco Fronzi and Roger D Amos and Rika Kobayashi and Naoki Matsumura
             and Kenta Watanabe and Rafael K Morizawa},
    title =    {{Evaluation of Machine Learning Interatomic Potentials for the
             Properties of Gold Nanoparticles}},
    journal =  {Nanomaterials (Basel).},
    year =     2022,
    volume =   12,
    issue =    21,
    pages =    3891,
    annote =   {We have investigated Machine Learning Interatomic Potentials in
             application to the properties of gold nanoparticles through the DeePMD
             package, using data generated with the ab-initio VASP program.
             Benchmarking was carried out on Au20 nanoclusters against ab-initio
             molecular dynamics simulations and show we can achieve similar
             accuracy with the machine learned potential at far reduced cost using
             LAMMPS. We have been able to reproduce structures and heat capacities
             of several isomeric forms. Comparison of our workflow with similar ML-
             IP studies is discussed and has identified areas for future
             improvement.},
    PMCID =    {PMC9655512},
    doi =      {10.3390/nano12213891},
}

@Article{Zhuang_JChemPhys_2022_v157_p164701,
    author =   {Yong-Bin Zhuang and Rui-Hao Bi and Jun Cheng},
    title =    {{Resolving the odd{\textendash}even oscillation of water dissociation
             at rutile TiO<sub>2</sub>(110){\textendash}water interface by machine
             learning accelerated molecular dynamics}},
    journal =  {J. Chem. Phys.},
    year =     2022,
    volume =   157,
    issue =    16,
    pages =    164701,
    annote =   {Aqueous rutile TiO2(110) is the most widely studied water-oxide
             interface, and yet questions about water dissociation are still
             controversial. Theoretical studies have systematically investigated
             the influence of the slab thickness on water dissociation energy
             (Ediss) at 1 monolayer coverage using static density functional theory
             calculation and found that Ediss exhibits odd-even oscillation with
             respect to the TiO2 slab thickness. However, less studies have
             accounted for the full solvation of an aqueous phase using ab initio
             molecular dynamics due to high computational costs in which only
             three, four, and five trilayer models of rutile(110)-water interfaces
             have been simulated. Here, we report Machine Learning accelerated
             Molecular Dynamics (MLMD) simulations of defect-free rutile(110)-water
             interfaces, which allows for a systematic study of the slab thickness
             ranging from 3 to 17 trilayers with much lower costs while keeping ab
             initio accuracy. Our MLMD simulations show that the dissociation
             degree of surface water ({\ensuremath{\alpha}}) oscillates with the
             slab thickness and converges to {\ensuremath{\sim}}2% as the TiO2 slab
             becomes thicker. Converting {\ensuremath{\alpha}} into dissociation
             free energy ({\ensuremath{\Delta}}Adiss) and comparing with
             dissociation total energy Ediss calculated with a single monolayer of
             water, we find that the full solvation of the interfaces suppresses
             surface water from dissociating. It is interesting to note that the
             machine learning potential trained from the dataset containing
             exclusively the five trilayer TiO2 model exhibits excellent
             transferability to other slab thicknesses and further captures the
             oscillating behavior of surface water dissociation. Detailed analyses
             indicate that the central plane in odd trilayer slabs modulates the
             interaction between double trilayers and, thus, the bonding strength
             between terminal Ti and water, which affects pKa of surface water and
             water dissociation degree.},
    doi =      {10.1063/5.0126333},
}

@Article{Li_SciAdv_2022_v8_peabq2900,
    author =   {Pai Li and Feng Ding},
    title =    {{Origin of the herringbone reconstruction of Au(111) surface at the
             atomic scale}},
    journal =  {Sci. Adv.},
    year =     2022,
    volume =   8,
    issue =    40,
    pages =    {eabq2900},
    annote =   {The origin of the herringbone reconstruction on Au(111) surface has
             never been explained properly at the atomic level because the large
             periodic length (~30 nm) does not allow ab initio simulations of the
             system and because of the lack of highly accurate empirical force
             field. We trained a machine learning force field with high accuracy to
             explore this reconstruction. Our study shows that the lattice
             deformation in Au deeper layers, which allows the effective relaxation
             of the densified and anisotropic top layer lattice, is critical for
             the herringbone reconstruction. The herringbone reconstruction is
             energetically more favorable than the stripe reconstruction only if
             the slab thickness exceeds 12 atomic layers. Furthermore, we reveal
             the high stability of herringbone reconstruction at high temperatures
             and that a slight strain of about {\ensuremath{\pm}}0.2% can induce a
             transition from the herringbone pattern to the stripe pattern, and
             both agree well with the experimental observations.},
    PMCID =    {PMC9534511},
    doi =      {10.1126/sciadv.abq2900},
}

@Article{Yang_ChinesePhysLett_2022_v39_p116301,
    author =   {Fenghu Yang and Qiyu Zeng and Bo Chen and Dongdong Kang and Shen Zhang
             and Jianhua Wu and Xiaoxiang Yu and Jiayu Dai},
    title =    {{Lattice Thermal Conductivity of MgSiO<sub>3</sub> Perovskite and Post-
             Perovskite under Lower Mantle Conditions Calculated by Deep Potential
             Molecular Dynamics}},
    journal =  {Chinese Phys. Lett.},
    year =     2022,
    volume =   39,
    issue =    11,
    pages =    116301,
    annote =   {<jats:p>Lattice thermal conductivity
             (<jats:italic>{\ensuremath{\kappa}}</jats:italic>
             <jats:sub>lat</jats:sub>) of MgSiO<jats:sub>3</jats:sub> perovskite
             and post-perovskite is an important parameter for the thermal dynamics
             in the Earth. Here, we develop a deep potential of density functional
             theory quality under entire thermodynamic conditions in the lower
             mantle, and calculate the
             <jats:italic>{\ensuremath{\kappa}}</jats:italic>
             <jats:sub>lat</jats:sub> by the Green{\textendash}Kubo relation. Deep
             potential molecular dynamics captures full-order anharmonicity and
             considers ill-defined phonons in
             low-<jats:italic>{\ensuremath{\kappa}}</jats:italic>
             <jats:sub>lat</jats:sub> materials ignored in the phonon gas model.
             The <jats:italic>{\ensuremath{\kappa}}</jats:italic>
             <jats:sub>lat</jats:sub> shows negative temperature dependence and
             positive linear pressure dependence. Interestingly, the
             <jats:italic>{\ensuremath{\kappa}}</jats:italic>
             <jats:sub>lat</jats:sub> undergos an increase at the phase boundary
             from perovskite to post-perovskite. We demonstrate that, along the
             geotherm, the <jats:italic>{\ensuremath{\kappa}}</jats:italic>
             <jats:sub>lat</jats:sub> increases by 18.2% at the phase boundary. Our
             results would be helpful for evaluating Earth{\textquoteright}s
             thermal dynamics and improving the Earth model.</jats:p>},
    doi =      {10.1088/0256-307X/39/11/116301},
}

@Article{Liu_Nanoscale_2023_v15_p285,
    author =   {Dongyu Liu and Yifan Wu and Andrey S Vasenko and Oleg V Prezhdo},
    title =    {{Grain boundary sliding and distortion on a nanosecond timescale induce
             trap states in CsPbBr<sub>3</sub>: <i>ab initio</i> investigation with
             machine learning force field}},
    journal =  {Nanoscale},
    year =     2023,
    volume =   15,
    issue =    1,
    pages =    {285--293},
    annote =   {Grain boundaries (GBs) in perovskite solar cells and optoelectronic
             devices are widely regarded as detrimental defects that accelerate
             charge and energy losses through nonradiative carrier trapping and
             recombination, but the mechanism is still under debate owing to the
             diversity of GB configurations and behaviors. We combine ab initio
             electronic structure and machine learning force field to investigate
             evolution of the geometric and electronic structure of a CsPbBr3 GB on
             a nanosecond timescale, which is comparable with the carrier
             recombination time. We demonstrate that the GB slides spontaneously
             within a few picoseconds increasing the band gap. Subsequent
             structural oscillations dynamically produce midgap trap states through
             Pb-Pb interactions across the GB. After several hundred picoseconds,
             structural distortions start to occur, increasing the occurrence of
             deep midgap states. We identify a distinct correlation of the average
             Pb-Pb distance and fluctuations in the ion coordination numbers with
             the appearance of the midgap states. Suppressing GB distortions
             through annealing and breaking up Pb-Pb dimers by passivation can
             efficiently alleviate the detrimental effects of GBs in perovskites.
             The study provides new insights into passivation of the detrimental GB
             defects, and demonstrates that structural and charge carrier dynamics
             in perovskites are intimately coupled.},
    doi =      {10.1039/d2nr05918e},
}

@Article{Liu_PhysChemChemPhys_2023_vNone_pNone,
    author =   {Jianchuan Liu and Renxi Liu and Yu Cao and Mohan Chen},
    title =    {{Solvation structures of calcium and magnesium ions in water with the
             presence of hydroxide: a study by deep potential molecular dynamics}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2023,
    annote =   {The solvation structures of calcium (Ca2+) and magnesium (Mg2+) ions
             with the presence of hydroxide (OH-) ion in water are essential for
             understanding their roles in biological and chemical processes but
             have not been fully explored. Ab initio molecular dynamics (AIMD) is
             an important tool to address this issue, but two challenges exist.
             First, an accurate description of OH- from AIMD needs an appropriate
             exchange-correlation functional. Second, a long trajectory is needed
             to reach an equilibrium state for the Ca2+-OH- and Mg2+-OH- ion pairs
             in aqueous solutions. Herein, we adopt a deep potential molecular
             dynamics (DPMD) method to simulate 1 ns trajectories for the Ca2+-OH-
             and Mg2+-OH- ion pairs in water; the DPMD method provides efficient
             machine-learning-based models that have the accuracy of the SCAN
             exchange-correlation functional within the framework of density
             functional theory. The solvation structures of the cations and the OH-
             in terms of three different species have been systematically
             investigated. On the one hand, we find that OH- have more significant
             effects on the solvation structure of Ca2+ than that of Mg2+. We
             observe that the OH- substantially affects the orientation angles of
             water molecules surrounding the cation. Through the time correlation
             functions, we conclude that the water molecules in the first solvation
             shell of Ca2+ change their preferred orientation faster than those of
             Mg2+. On the other hand, with the presence of the cation in the first
             solvation shell of OH-, we find that the hydrogen bonds of OH- are
             severely altered, and the adjacent water molecules of OH- are
             squeezed. The two cations have substantially different effects on the
             solvation structure of OH-. Our work provides new insight into the
             solvation structures of Ca2+ and Mg2+ in water with the presence of
             OH-.},
    doi =      {10.1039/d2cp04105g},
}

@Article{Urata_JPhysChemC_2022_v126_p21507,
    author =   {Shingo Urata},
    title =    {{Modeling Short-Range and Three-Membered Ring Structures in Lithium
             Borosilicate Glasses Using a Machine-Learning Potential}},
    journal =  {J. Phys. Chem. C},
    year =     2022,
    volume =   126,
    issue =    50,
    pages =    {21507--21517},
    doi =      {10.1021/acs.jpcc.2c07597},
}

@Article{Wu_ACSApplMaterInterfaces_2022_v14_p55753,
    author =   {Yifan Wu and Dongyu Liu and Weibin Chu and Bipeng Wang and Andrey S
             Vasenko and Oleg V Prezhdo},
    title =    {{Fluctuations at Metal Halide Perovskite Grain Boundaries Create
             Transient Trap States: Machine Learning Assisted Ab Initio Analysis}},
    journal =  {ACS Appl. Mater. Interfaces},
    year =     2022,
    volume =   14,
    issue =    50,
    pages =    {55753--55761},
    annote =   {All-inorganic perovskites are promising candidates for solar energy
             and optoelectronic applications, despite their polycrystalline nature
             with a large density of grain boundaries (GBs) due to facile solution-
             processed fabrication. GBs exhibit complex atomistic structures
             undergoing slow rearrangements. By studying evolution of the
             {\ensuremath{\Sigma}}5(210) CsPbBr3 GB on a nanosecond time scale,
             comparable to charge carrier lifetimes, we demonstrate that GB
             deformations appear every {\ensuremath{\sim}}100 ps and increase
             significantly the probability of deep charge traps. However, the deep
             traps form only transiently for a few hundred femtoseconds. In
             contrast, shallow traps appear continuously at the GB. Shallow traps
             are localized in the GB layer, while deep traps are in a sublayer,
             which is still distorted from the pristine structure and can be jammed
             in unfavorable conformations. The GB electronic properties correlate
             with bond angles, with notable exception of the Br-Br distance, which
             provides a signature of halide migration along GBs. The transient
             nature of trap states and localization of electrons and holes at
             different parts of GBs indicate that charge carrier lifetimes should
             be long. At the same time, charge mobility can be reduced. The
             complex, multiscale evolution of geometric and electronic structures
             of GBs rationalize the contradictory statements made in the literature
             regarding both benign and detrimental roles of GBs in perovskite
             performance and provide new atomistic insights into perovskite
             properties.},
    doi =      {10.1021/acsami.2c16203},
}

@Article{Mondal_JChemTheoryComput_2022_vNone_pNone,
    author =   {Anirban Mondal and Dina Kussainova and Shuwen Yue and Athanassios Z
             Panagiotopoulos},
    title =    {{Modeling Chemical Reactions in Alkali Carbonate{\textendash}Hydroxide
             Electrolytes with Deep Learning Potentials}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    annote =   {We developed a deep potential machine learning model for simulations
             of chemical reactions in molten alkali carbonate-hydroxide electrolyte
             containing dissolved CO2, using an active learning procedure. We
             tested the deep neural network (DNN) potential and training procedure
             against reaction kinetics, chemical composition, and diffusion
             coefficients obtained from density functional theory (DFT) molecular
             dynamics calculations. The DNN potential was found to match DFT
             results for the structural, transport, and short-time chemical
             reactions in the melt. Using the DNN potential, we extended the time
             scales of observation to 2 ns in systems containing thousands of
             atoms, while preserving quantum chemical accuracy. This allowed us to
             reach chemical equilibrium with respect to several chemical species in
             the melt. The approach can be generalized for a broad spectrum of
             chemically reactive systems.},
    doi =      {10.1021/acs.jctc.2c00816},
}

@Article{Pitike_JournalofNuclearMaterials_2023_v574_p154183,
    author =   {Krishna Chaitanya Pitike and Wahyu Setyawan},
    title =    {{Accurate Fe{\textendash}He machine learning potential for studying He
             effects in BCC-Fe}},
    journal =  {Journal of Nuclear Materials},
    year =     2023,
    volume =   574,
    pages =    154183,
    doi =      {10.1016/j.jnucmat.2022.154183},
}

@Article{Kobayashi_ChemCommun_2022_v58_p13939,
    author =   {Takeshi Kobayashi and Da-Jiang Liu and Fr{\'e}d{\'e}ric A Perras},
    title =    {{Spatial arrangement of dynamic surface species from solid-state NMR
             and machine learning-accelerated MD simulations}},
    journal =  {Chem. Commun. (Camb).},
    year =     2022,
    volume =   58,
    issue =    100,
    pages =    {13939--13942},
    annote =   {The surface arrangement of motional organic functionalities is
             explored by experimental dipolar coupling measurements and the
             prediction of motionally-averaged coupling constant from molecular
             dynamics simulations. The use of machine learning potentials was key
             to reaching the timescale required. The distance between dynamic
             surface species are important in cooperative heterogeneous catalysis.},
    doi =      {10.1039/d2cc05861h},
}

@Article{Huang_EnergyandAI_2023_v11_p100210,
    author =   {Xiaona Huang and Kun Luo and Yidi Shen and Yanan Yue and Qi An},
    title =    {{Grain boundaries induce significant decrease in lattice thermal
             conductivity of CdTe}},
    journal =  {Energy and AI},
    year =     2023,
    volume =   11,
    pages =    100210,
    doi =      {10.1016/j.egyai.2022.100210},
}
@Article{Ling_JournalofPowerSources_2023_v555_p232350,
    author =   {Yulong Ling and Kun Li and Mi Wang and Junfeng Lu and Chenlu Wang and
             Yanlei Wang and Hongyan He},
    title =    {{Revisiting the structure, interaction, and dynamical property of ionic
             liquid from the deep learning force field}},
    journal =  {Journal of Power Sources},
    year =     2023,
    volume =   555,
    pages =    232350,
    doi =      {10.1016/j.jpowsour.2022.232350},
}
@Article{Li_JournaloftheEuropeanCeramicSociety_2023_v43_p208,
    author =   {Jun Li and Qi An},
    title =    {{Quasiplastic deformation in shocked nanocrystalline boron carbide:
             Grain boundary sliding and local amorphization}},
    journal =  {Journal of the European Ceramic Society},
    year =     2023,
    volume =   43,
    issue =    2,
    pages =    {208--216},
    doi =      {10.1016/j.jeurceramsoc.2022.10.014},
}

@Article{Li_GeophysicalResearchLetters_2022_v49_pNone,
    author =   {Zhi Li and Sandro Scandolo},
    title =    {{Elasticity and Viscosity of hcp Iron at Earth's Inner Core Conditions
             From Machine Learning{-}Based Large{-}Scale Atomistic Simulations}},
    journal =  {Geophysical Research Letters},
    year =     2022,
    volume =   49,
    issue =    24,
    annote =   {<jats:title>Abstract</jats:title><jats:p>Although considerable efforts
             have been made in the last years to examine the physical properties of
             hexagonal close{-}packed (hcp) iron at extreme conditions, it remains
             challenging to explain many geophysical observations in Earth's inner
             core. Here we examine the elastic and plastic behavior of hcp iron and
             the effects of structural defects at inner core conditions using
             large{-}scale atomistic simulations coupled with machine
             learning{-}based interatomic potential. Our results suggest that the
             seismic anisotropy pattern in the inner core can be ascribed to the
             elastic anisotropy (6%) of hcp iron. The observed low shear wave
             velocity is largely produced by viscous grain boundaries in iron
             polycrystal. We also found highly mobile and abundant vacancies in hcp
             iron yield a viscous strength
             (10<jats:sup>15{\ensuremath{\pm}}1</jats:sup>) that is consistent with
             the geophysical observations. Therefore, our findings highlight the
             role played by structural defects and lessen the demand for light
             elements to explain the observed seismic data.</jats:p>},
    doi =      {10.1029/2022GL101161},
}


@Article{Chahal_JACSAu_2022_v2_p2693,
    author =   {Rajni Chahal and Santanu Roy and Martin Brehm and Shubhojit Banerjee
             and Vyacheslav Bryantsev and Stephen T Lam},
    title =    {{Transferable Deep Learning Potential Reveals Intermediate-Range
             Ordering Effects in LiF{\textendash}NaF{\textendash}ZrF<sub>4</sub>
             Molten Salt}},
    journal =  {JACS Au},
    year =     2022,
    volume =   2,
    issue =    12,
    pages =    {2693--2702},
    annote =   {LiF-NaF-ZrF4 multicomponent molten salts are promising candidate
             coolants for advanced clean energy systems owing to their desirable
             thermophysical and transport properties. However, the complex
             structures enabling these properties, and their dependence on
             composition, is scarcely quantified due to limitations in simulating
             and interpreting experimental spectra of highly disordered,
             intermediate-ranged structures. Specifically, size-limited ab initio
             simulations and accuracy-limited classical models used in the past are
             unable to capture a wide range of fluctuating motifs found in the
             extended heterogeneous structures of liquid salt. This greatly
             inhibits our ability to design tailored compositions and materials.
             Here, accurate, efficient, and transferable machine learning
             potentials are used to predict structures far beyond the first
             coordination shell in LiF-NaF-ZrF4. Neural networks trained at only
             eutectic compositions with 29% and 37% ZrF4 are shown to accurately
             simulate a wide range of compositions (11-40% ZrF4) with dramatically
             different coordination chemistries, while showing a remarkable
             agreement with theoretical and experimental Raman spectra. The
             theoretical Raman calculations further uncovered the previously unseen
             shift and flattening of bending band at {\ensuremath{\sim}}250 cm-1
             which validated the simulated extended-range structures as observed in
             compositions with higher than 29% ZrF4 content. In such cases, machine
             learning-based simulations capable of accessing larger time and length
             scales (beyond 17 {\r{A}}) were critical for accurately predicting
             both structure and ionic diffusivities.},
    PMCID =    {PMC9795562},
    doi =      {10.1021/jacsau.2c00526},
}


@Article{Li_ActaPhysSin_2022_v71_p247803,
    author =   {Zhi-Qiang Li and Xiao-Yu Tan and Xin-Lei Duan and Jing-Yi Zhang and
             Jia-Yue Yang},
    title =    {{Deep learning molecular dynamics simulation on microwave high-
             temperature dielectric function of silicon nitride}},
    journal =  {Acta Phys. Sin.},
    year =     2022,
    volume =   71,
    issue =    24,
    pages =    247803,
    annote =   {<jats:p xml:lang="en">Silicon nitride (&lt;i&gt;{\ensuremath{\beta}}&l
             t;/i&gt;-Si&lt;sub&gt;3&lt;/sub&gt;N&lt;sub&gt;4&lt;/sub&gt;) is a
             most promising thermal wave-transparent material. The accurate
             measurement of its high-temperature dielectric function is essential
             to solving the {\textquotedblleft}black barrier{\textquotedblright}
             problem of hypersonic vehicles and accelerating the design of silicon
             nitride-based thermal wave-transparent materials. Direct experimental
             measurement at high temperature is a difficult job and the accuracy of
             classical molecular dynamics (CMD) simulations suffers the choice of
             empirical potential. In this work, we build a &lt;i&gt;{\ensuremath{\b
             eta}}&lt;/i&gt;-Si&lt;sub&gt;3&lt;/sub&gt;N&lt;sub&gt;4&lt;/sub&gt;
             model on a nanoscale, train the deep learning potential (DLP) by using
             first-principles data, and apply the deep potential molecular dynamics
             (DPMD) to simulate the polarization relaxation process. The predicted
             energy and force by DLP are excellently consistent with first-
             principles calculations, which proves the high accuracy of DLP. The
             RMSEs for &lt;i&gt;{\ensuremath{\beta}}&lt;/i&gt;-
             Si&lt;sub&gt;3&lt;/sub&gt;N&lt;sub&gt;4&lt;/sub&gt; are quite low
             (0.00550 meV/atom for energy and 7.800 meV/{\r{A}} for force).
             According to the Cole-Cole formula, the microwave dielectric function
             in the temperature range of 300{\textendash}1000 K is calculated by
             using the deep learning molecular dynamics method. Compared with the
             empirical potential, the computational results of the DLP are
             consistent with the experimental results in the sense of order of
             magnitude. It is also found that the DPMD performs well in terms of
             computational speed. In addition, a mathematical model of the
             temperature dependence of the relaxation time is established to reveal
             the pattern of relaxation time varying with temperature. The high-
             temperature microwave dielectric function of silicon nitride is
             calculated by implementing large-scale and high-precision molecular
             dynamics simulations. It provides fundamental data for promoting the
             application of silicon nitride in high-temperature thermal
             transmission.</jats:p>},
    doi =      {10.7498/aps.71.20221002},
}


@Article{Fu_JMaterChemA_2023_v11_p742,
    author =   {Shubin Fu and Dizhou Liu and Yuanpeng Deng and Menglin Li and Han Zhao
             and Jingran Guo and Jian Zhou and Pengyu Zhang and Chong Wang and
             Hongxuan Yu and Shixuan Dang and Jianing Zhang and Menglong Hao and
             Hui Li and Xiang Xu},
    title =    {{Medium-entropy ceramic aerogels for robust thermal sealing}},
    journal =  {J. Mater. Chem. A},
    year =     2023,
    volume =   11,
    issue =    2,
    pages =    {742--752},
    annote =   {<jats:p>MECA fabricated by far-field electrospinning exhibit excellent
             thermomechanical stability due to the medium entropy effects and
             superior high temperature thermal insulation performance due to the
             thermal radiation reflection of TiO<jats:sub>2</jats:sub>.</jats:p>},
    doi =      {10.1039/d2ta08264k},
}


@Article{Li_PhysRevApplied_2022_v18_p064067,
    author =   {Tingwei Li and Peng-Hu Du and Ling Bai and Qiang Sun and Puru Jena},
    title =    {{Thermoelectric Figure of Merit of a Superatomic Crystal  ReSeI2
                  Monolayer}},
    journal =  {Phys. Rev. Applied},
    year =     2022,
    volume =   18,
    issue =    6,
    pages =    064067,
    doi =      {10.1103/PhysRevApplied.18.064067},
}


@Article{Jiang_NatCommun_2022_v13_p6067,
    author =   {Shuai Jiang and Yi-Rong Liu and Teng Huang and Ya-Juan Feng and Chun-
             Yu Wang and Zhong-Quan Wang and Bin-Jing Ge and Quan-Sheng Liu and
             Wei-Ran Guang and Wei Huang},
    title =    {{Towards fully ab initio simulation of atmospheric aerosol nucleation}},
    journal =  {Nat. Commun.},
    year =     2022,
    volume =   13,
    issue =    1,
    pages =    6067,
    annote =   {Atmospheric aerosol nucleation contributes to approximately half of
             the worldwide cloud condensation nuclei. Despite the importance of
             climate, detailed nucleation mechanisms are still poorly understood.
             Understanding aerosol nucleation dynamics is hindered by the
             nonreactivity of force fields (FFs) and high computational costs due
             to the rare event nature of aerosol nucleation. Developing reactive
             FFs for nucleation systems is even more challenging than developing
             covalently bonded materials because of the wide size range and high
             dimensional characteristics of noncovalent hydrogen bonding bridging
             clusters. Here, we propose a general workflow that is also applicable
             to other systems to train an accurate reactive FF based on a deep
             neural network (DNN) and further bridge DNN-FF-based molecular
             dynamics (MD) with a cluster kinetics model based on Poisson
             distributions of reactive events to overcome the high computational
             costs of direct MD. We found that previously reported acid-base
             formation rates tend to be significantly underestimated, especially in
             polluted environments, emphasizing that acid-base nucleation observed
             in multiple environments should be revisited.},
    PMCID =    {PMC9568664},
    doi =      {10.1038/s41467-022-33783-y},
}


@Article{Bayerl_DigitalDiscovery_2022_v1_p61,
    author =   {Dylan Bayerl and Christopher M. Andolina and Shyam Dwaraknath and
             Wissam A. Saidi},
    title =    {{Convergence acceleration in machine learning potentials for atomistic
             simulations}},
    journal =  {Digital Discovery},
    year =     2022,
    volume =   1,
    issue =    1,
    pages =    {61--69},
    annote =   {<jats:p>Machine learning potentials (MLPs) for atomistic simulations
             have an enormous prospective impact on materials modeling, offering
             orders of magnitude speedup over density functional theory simulations
             without appreciably sacrificing accuracy of material property
             prediction.</jats:p>},
    doi =      {10.1039/d1dd00005e},
}


@Article{Xu_ACSApplMaterInterfaces_2023_vNone_pNone,
    author =   {Tingrui Xu and Xuejiao Li and Yang Wang and Zhongfeng Tang},
    title =    {{Development of Deep Potentials of Molten
             MgCl<sub>2</sub>{\textendash}NaCl and MgCl<sub>2</sub>{\textendash}KCl
             Salts Driven by Machine Learning}},
    journal =  {ACS Appl. Mater. Interfaces},
    year =     2023,
    annote =   {Molten MgCl2-based chlorides have emerged as potential thermal storage
             and heat transfer materials due to high thermal stabilities and lower
             costs. In this work, deep potential molecular dynamics (DPMD)
             simulations by a method combination of the first principle, classical
             molecular dynamics, and machine learning are performed to systemically
             study the relationships of structures and thermophysical properties of
             molten MgCl2-NaCl (MN) and MgCl2-KCl (MK) eutectic salts at the
             temperature range of 800-1000 K. The densities, radial distribution
             functions, coordination numbers, potential mean forces, specific heat
             capacities, viscosities, and thermal conductivities of these two
             chlorides are successfully reproduced under extended temperatures by
             DPMD with a larger size (5.2 nm) and longer timescale (5 ns). It is
             concluded that the higher specific heat capacity of molten MK is
             originated from the strong potential mean force of Mg-Cl bonds,
             whereas the molten MN performs better in heat transfer due to the
             larger thermal conductivity and lower viscosity, attributed to the
             weak interaction between Mg and Cl ions. Innovatively, the
             plausibility and reliability of microscopic structures and macroscopic
             properties for molten MN and MK verify the extensibilities of these
             two deep potentials in temperatures, and these DPMD results also
             provide detailed technical parameters to the simulations of other
             formulated MN and MK salts.},
    doi =      {10.1021/acsami.2c19272},
}


@Article{Li_JPhysCondensMatter_2023_v35_p505001,
    author =   {Wentao Li and Chenxiu Yang},
    title =    {{Thermal conductivity of van der Waals heterostructure of 2D GeS and
             SnS based on machine learning interatomic potential}},
    journal =  {J. Phys. Condens. Matter},
    year =     2023,
    volume =   35,
    issue =    50,
    pages =    505001,
    annote =   {van der Waals heterostructures have provided an unprecedented platform
             to tune many physical properties for two-dimensional materials. In
             this work, thermal transport properties of van der Waals
             heterostructures formed by vertical stacking of monolayers GeS and SnS
             have been investigated systematically based on machine learning
             interatomic potential. The effect of van der Waals interface on the
             lattice thermal transport of 2D SnS and GeS can be well clarified by
             introducing various stacking configurations. Our results indicate that
             the van der Waals interface can strongly suppress the thermal
             transport capacity for the considered heterostructures, and either the
             average thermal conductivity per layer or the 2D thermal sheet
             conductance for the considered heterostructures is lower than that of
             corresponding monolayers. The suppressed thermal conductivity with
             tunable in-plane anisotropy in SnS/GeS heterostructures can be
             ascribed to the enhanced interface anharmonic scattering, and thus
             exhibits obvious interface-dependent characteristics. Therefore, this
             work highlights that the van der Waals interface can be employed to
             effectively modulate thermal transport for the 2D puckered group-IV
             monochalcogenides, and the suppressed lattice thermal conductivity
             together with interface-dependent phonon transport properties in the
             SnS/GeS heterostructure imply the great potential for corresponding
             thermoelectrical applications.},
    doi =      {10.1088/1361-648X/acf6ea},
}


@Article{Qi_JournalofNonCrystallineSolids_2023_v622_p122682,
    author =   {Yongnian Qi and Xiaoguang Guo and Ming Li and Chongkun Wang and Qing
             Mu and Ping Zhou},
    title =    {{Reversible and irreversible photon-absorption in amorphous SiO2
             revealed by deep potential}},
    journal =  {Journal of Non-Crystalline Solids},
    year =     2023,
    volume =   622,
    pages =    122682,
    doi =      {10.1016/j.jnoncrysol.2023.122682},
}


@Article{Liang_InternationalJournalofHeatandMassTransfer_2023_v217_p124705,
    author =   {Fei Liang and Jing Ding and Xiaolan Wei and Gechuanqi Pan and Shule
             Liu},
    title =    {{Interfacial heat and mass transfer at silica/binary molten salt
             interface from deep potential molecular dynamics}},
    journal =  {International Journal of Heat and Mass Transfer},
    year =     2023,
    volume =   217,
    pages =    124705,
    doi =      {10.1016/j.ijheatmasstransfer.2023.124705},
}


@Article{Gupta_NatCommun_2023_v14_p6884,
    author =   {Sunny Gupta and Xiaochen Yang and Gerbrand Ceder},
    title =    {{What dictates soft clay-like lithium superionic conductor formation
             from rigid salts mixture}},
    journal =  {Nat. Commun.},
    year =     2023,
    volume =   14,
    issue =    1,
    pages =    6884,
    annote =   {Soft clay-like Li-superionic conductors, integral to realizing all-
             solid-state batteries, have been recently synthesized by mixing rigid-
             salts. Here, through computational and experimental analysis, we
             clarify how a soft clay-like material can be created from a mixture of
             rigid-salts. Using molecular dynamics simulations with a deep
             learning-based interatomic potential energy model, we uncover the
             microscopic features responsible for soft clay-formation from ionic
             solid mixtures. We find that salt mixtures capable of forming
             molecular solid units on anion exchange, along with the slow kinetics
             of such reactions, are key to soft-clay formation. Molecular solid
             units serve as sites for shear transformation zones, and their
             inherent softness enables plasticity at low stress. Extended X-ray
             absorption fine structure spectroscopy confirms the formation of
             molecular solid units. A general strategy for creating soft clay-like
             materials from ionic solid mixtures is formulated.},
    PMCID =    {PMC10613223},
    doi =      {10.1038/s41467-023-42538-2},
}


@Article{Zhang_ActaMaterialia_2023_v261_p119364,
    author =   {Jin-Yu Zhang and Zhi-Peng Sun and Dong Qiu and Fu-Zhi Dai and Yang-
             Sheng Zhang and Dongsheng Xu and Wen-Zheng Zhang},
    title =    {{Dislocation-mediated migration of the
             {\ensuremath{\alpha}}/{\ensuremath{\beta}} interfaces in titanium}},
    journal =  {Acta Materialia},
    year =     2023,
    volume =   261,
    pages =    119364,
    doi =      {10.1016/j.actamat.2023.119364},
}


@Article{Liu_npjComputMater_2023_v9_p174,
    author =   {Yunsheng Liu and Xingfeng He and Yifei Mo},
    title =    {{Discrepancies and error evaluation metrics for machine learning
             interatomic potentials}},
    journal =  {npj Comput Mater},
    year =     2023,
    volume =   9,
    issue =    1,
    pages =    174,
    annote =   {<jats:title>Abstract</jats:title><jats:p>Machine learning interatomic
             potentials (MLIPs) are a promising technique for atomic modeling.
             While small errors are widely reported for MLIPs, an open concern is
             whether MLIPs can accurately reproduce atomistic dynamics and related
             physical properties in molecular dynamics (MD) simulations. In this
             study, we examine the state-of-the-art MLIPs and uncover several
             discrepancies related to atom dynamics, defects, and rare events
             (REs), compared to ab initio methods. We find that low averaged errors
             by current MLIP testing are insufficient, and develop quantitative
             metrics that better indicate the accurate prediction of atomic
             dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation
             metrics are demonstrated to have improved prediction in multiple
             properties. The identified errors, the evaluation metrics, and the
             proposed process of developing such metrics are general to MLIPs, thus
             providing valuable guidance for future testing and improvements of
             accurate and reliable MLIPs for atomistic modeling.</jats:p>},
    doi =      {10.1038/s41524-023-01123-3},
}


@Article{Lin_NatCommun_2023_v14_p4110,
    author =   {Bo Lin and Jian Jiang and Xiao Cheng Zeng and Lei Li},
    title =    {{Temperature-pressure phase diagram of confined monolayer water/ice at
             first-principles accuracy with a machine-learning force field}},
    journal =  {Nat. Commun.},
    year =     2023,
    volume =   14,
    issue =    1,
    pages =    4110,
    annote =   {Understanding the phase behaviour of nanoconfined water films is of
             fundamental importance in broad fields of science and engineering.
             However, the phase behaviour of the thinnest water film - monolayer
             water - is still incompletely known. Here, we developed a machine-
             learning force field (MLFF){~}at first-principles accuracy to
             determine the phase diagram of monolayer water/ice in nanoconfinement
             with hydrophobic walls. We observed the spontaneous formation of two
             previously unreported high-density ices, namely, zigzag quasi-bilayer
             ice (ZZ-qBI) and branched-zigzag quasi-bilayer ice (bZZ-qBI). Unlike
             conventional bilayer ices, few inter-layer hydrogen bonds were
             observed in both quasi-bilayer ices. Notably, the bZZ-qBI entails a
             unique hydrogen-bonding network that consists of two distinctive types
             of hydrogen bonds. Moreover, we identified, for the first time, the
             stable region for the lowest-density [Formula: see text] monolayer ice
             (LD-48MI) at negative pressures (<-0.3{\,}GPa). Overall, the MLFF
             enables large-scale first-principle-level molecular dynamics (MD)
             simulations of the spontaneous transition from the liquid water to a
             plethora of monolayer ices, including hexagonal, pentagonal, square,
             zigzag (ZZMI), and hexatic monolayer ices. These findings will enrich
             our understanding of the phase behaviour of the nanoconfined
             water/ices and provide a guide for future experimental realization of
             the 2D ices.},
    PMCID =    {PMC10336112},
    doi =      {10.1038/s41467-023-39829-z},
}


@Article{Lu_NatCommun_2023_v14_p4077,
    author =   {Pushun Lu and Yu Xia and Guochen Sun and Dengxu Wu and Siyuan Wu and
             Wenlin Yan and Xiang Zhu and Jiaze Lu and Quanhai Niu and Shaochen Shi
             and Zhengju Sha and Liquan Chen and Hong Li and Fan Wu},
    title =    {{Realizing long-cycling all-solid-state Li-In||TiS2 batteries using
             Li6+xMxAs1-xS5I (M=Si, Sn) sulfide solid electrolytes}},
    journal =  {Nat. Commun.},
    year =     2023,
    volume =   14,
    issue =    1,
    pages =    4077,
    annote =   {Inorganic sulfide solid-state electrolytes, especially Li6PS5X (X =
             Cl, Br, I), are considered viable materials for developing all-solid-
             state batteries because of their high ionic conductivity and low cost.
             However, this class of solid-state electrolytes suffers from
             structural and chemical instability in humid air environments and a
             lack of compatibility with layered oxide positive electrode active
             materials. To circumvent these issues, here, we propose
             Li6+xMxAs1-xS5I (M=Si, Sn) as sulfide solid electrolytes. When the
             Li6+xSixAs1-xS5I (x{\,}={\,}0.8) is tested in combination with a Li-In
             negative electrode and Ti2S-based positive electrode at
             30{\,}{\textdegree}C and 30{\,}MPa, the Li-ion lab-scale Swagelok
             cells demonstrate long cycle life of almost 62500 cycles at
             2.44{\,}mA{\,}cm-2, decent power performance (up to
             24.45{\,}mA{\,}cm-2) and areal capacity of 9.26 mAh cm-2 at
             0.53{\,}mA{\,}cm-2.},
    PMCID =    {PMC10333182},
    doi =      {10.1038/s41467-023-39686-w},
}


@Article{Bore_NatCommun_2023_v14_p3349,
    author =   {Sigbj{\o}rn L{\o}land Bore and Francesco Paesani},
    title =    {{Realistic phase diagram of water from {\textquotedblleft}first
             principles{\textquotedblright} data-driven quantum simulations}},
    journal =  {Nat. Commun.},
    year =     2023,
    volume =   14,
    issue =    1,
    pages =    3349,
    annote =   {Since the experimental characterization of the low-pressure region of
             water's phase diagram in the early 1900s, scientists have been on a
             quest to understand the thermodynamic stability of ice polymorphs on
             the molecular level. In this study, we demonstrate that combining the
             MB-pol data-driven many-body potential for water, which was rigorously
             derived from "first principles" and exhibits chemical accuracy, with
             advanced enhanced-sampling algorithms, which correctly describe the
             quantum nature of molecular motion and thermodynamic equilibria,
             enables computer simulations of water's phase diagram with an
             unprecedented level of realism. Besides providing fundamental insights
             into how enthalpic, entropic, and nuclear quantum effects shape the
             free-energy landscape of water, we demonstrate that recent progress in
             "first principles" data-driven simulations, which rigorously encode
             many-body molecular interactions, has opened the door to realistic
             computational studies of complex molecular systems, bridging the gap
             between experiments and simulations.},
    PMCID =    {PMC10250386},
    doi =      {10.1038/s41467-023-38855-1},
}


@Article{Wang_NatCommun_2023_v14_p2924,
    author =   {Xiaoyang Wang and Zhenyu Wang and Pengyue Gao and Chengqian Zhang and
             Jian Lv and Han Wang and Haifeng Liu and Yanchao Wang and Yanming Ma},
    title =    {{Data-driven prediction of complex crystal structures of dense lithium}},
    journal =  {Nat. Commun.},
    year =     2023,
    volume =   14,
    issue =    1,
    pages =    2924,
    annote =   {Lithium (Li) is a prototypical simple metal at ambient conditions, but
             exhibits remarkable changes in structural and electronic properties
             under compression. There has been intense debate about the structure
             of dense Li, and recent experiments offered fresh evidence for yet
             undetermined crystalline phases near the enigmatic melting minimum
             region in the pressure-temperature phase diagram of Li. Here, we
             report on an extensive exploration of the energy landscape of Li using
             an advanced crystal structure search method combined with a machine-
             learning approach, which greatly expands the scale of structure
             search, leading to the prediction of four complex Li crystal
             structures containing up to 192 atoms in the unit cell that are
             energetically competitive with known Li structures. These findings
             provide a viable solution to the observed yet unidentified crystalline
             phases of Li, and showcase the predictive power of the global
             structure search method for discovering complex crystal structures in
             conjunction with accurate machine learning potentials.},
    PMCID =    {PMC10203143},
    doi =      {10.1038/s41467-023-38650-y},
}


@Article{Sun_NatCommun_2023_v14_p1656,
    author =   {Shichuan Sun and Yu He and Junyi Yang and Yufeng Lin and Jinfeng Li
             and Duck Young Kim and Heping Li and Ho-Kwang Mao},
    title =    {{Superionic effect and anisotropic texture in Earth{\textquoteright}s
             inner core driven by geomagnetic field}},
    journal =  {Nat. Commun.},
    year =     2023,
    volume =   14,
    issue =    1,
    pages =    1656,
    annote =   {Seismological observations suggest that Earth's inner core (IC) is
             heterogeneous and anisotropic. Increasing seismological observations
             make the understanding of the mineralogy and mechanism for the complex
             IC texture extremely challenging, and the driving force for the
             anisotropic texture remains unclear. Under IC conditions, hydrogen
             becomes highly diffusive like liquid in the hexagonal-close-packed
             (hcp) solid Fe lattice, which is known as the superionic state. Here,
             we reveal that H-ion diffusion in superionic Fe-H alloy is anisotropic
             with the lowest barrier energy along the c-axis. In the presence of an
             external electric field, the alignment of the Fe-H lattice with the
             c-axis pointing to the field direction is energetically favorable. Due
             to this effect, Fe-H alloys are aligned with the c-axis parallel to
             the equatorial plane by the diffusion of the north-south dipole
             geomagnetic field into the inner core. The aligned texture driven by
             the geomagnetic field presents significant seismic anisotropy, which
             explains the anisotropic seismic velocities in the IC, suggesting a
             strong coupling between the IC structure and geomagnetic field.},
    PMCID =    {PMC10039083},
    doi =      {10.1038/s41467-023-37376-1},
}


@Article{Wu_JChemPhys_2023_v159_pNone,
    author =   {Haiyi Wu and Chenxing Liang and Jinu Jeong and N R Aluru},
    title =    {{From <i>ab{~}initio</i> to continuum: Linking multiple scales using
             deep-learned forces}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    18,
    annote =   {We develop a deep learning-based algorithm, called DeepForce, to link
             ab{~}initio physics with the continuum theory to predict concentration
             profiles of confined water. We show that the deep-learned forces can
             be used to predict the structural properties of water confined in a
             nanochannel with quantum scale accuracy by solving the continuum
             theory given by Nernst-Planck equation. The DeepForce model has an
             excellent predictive performance with a relative error less than 7.6%
             not only for confined water in small channel systems (L < 6{~}nm) but
             also for confined water in large channel systems (L = 20{~}nm) which
             are computationally inaccessible through the high accuracy ab{~}initio
             molecular dynamics simulations. Finally, we note that classical
             Molecular dynamics simulations can be inaccurate in capturing the
             interfacial physics of water in confinement (L < 4.0{~}nm) when
             quantum scale physics are neglected.},
    doi =      {10.1063/5.0166927},
}


@Article{Zhang_EnergyStorageMaterials_2023_v63_p103069,
    author =   {Yifeng Zhang and Hui Huang and Jie Tian and Chengwei Li and Yuchen
             Jiang and Zeng Fan and Lujun Pan},
    title =    {{Modelling electrified microporous carbon/electrolyte electrochemical
             interface and unraveling charge storage mechanism by machine learning
             accelerated molecular dynamics}},
    journal =  {Energy Storage Materials},
    year =     2023,
    volume =   63,
    pages =    103069,
    doi =      {10.1016/j.ensm.2023.103069},
}


@Article{Deng_TheoreticalandAppliedMechanicsLetters_2023_v13_p100481,
    author =   {Yuanpeng Deng and Chong Wang and Xiang Xu and Hui Li},
    title =    {{Machine learning potential for Ab Initio phase transitions of zirconia}},
    journal =  {Theoretical and Applied Mechanics Letters},
    year =     2023,
    volume =   13,
    issue =    6,
    pages =    100481,
    doi =      {10.1016/j.taml.2023.100481},
}


@Article{Dai_NatEnergy_2023_v8_p1221,
    author =   {Tao Dai and Siyuan Wu and Yaxiang Lu and Yang Yang and Yuan Liu and
             Chao Chang and Xiaohui Rong and Ruijuan Xiao and Junmei Zhao and
             Yanhui Liu and Weihua Wang and Liquan Chen and Yong-Sheng Hu},
    title =    {{Inorganic glass electrolytes with polymer-like viscoelasticity}},
    journal =  {Nat Energy},
    year =     2023,
    volume =   8,
    issue =    11,
    pages =    {1221--1228},
    doi =      {10.1038/s41560-023-01356-y},
}


@Article{Wang_EarthandPlanetaryScienceLetters_2023_v621_p118368,
    author =   {Dong Wang and Zhongqing Wu and Xin Deng},
    title =    {{Thermal conductivity of Fe-bearing bridgmanite and post-perovskite:
             Implications for the heat flux from the core}},
    journal =  {Earth and Planetary Science Letters},
    year =     2023,
    volume =   621,
    pages =    118368,
    doi =      {10.1016/j.epsl.2023.118368},
}


@Article{Hu_SciChinaChem_2023_v66_p3297,
    author =   {Youcheng Hu and Xiaoxiao Wang and Peng Li and Junxiang Chen and
             Shengli Chen},
    title =    {{Understanding the sluggish and highly variable transport kinetics of
             lithium ions in LiFePO4}},
    journal =  {Sci. China Chem.},
    year =     2023,
    volume =   66,
    issue =    11,
    pages =    {3297--3306},
    doi =      {10.1007/s11426-023-1662-9},
}


@Article{Liu_ChemicalEngineeringJournal_2023_v474_p145355,
    author =   {Xi Liu and Wei Sun and Xiang Hu and Junxiang Chen and Zhenhai Wen},
    title =    {{Self-powered H2 generation implemented by hydrazine oxidation
             assisting hybrid electrochemical cell}},
    journal =  {Chemical Engineering Journal},
    year =     2023,
    volume =   474,
    pages =    145355,
    doi =      {10.1016/j.cej.2023.145355},
}


@Article{He_SolidStateIonics_2023_v399_p116298,
    author =   {Yining He and Qian Chen and Wei Lai},
    title =    {{Computational study of Na diffusion and conduction in P2- and
             O3-Na2x[NixTi1-x]O2 materials with machine-learning interatomic
             potentials}},
    journal =  {Solid State Ionics},
    year =     2023,
    volume =   399,
    pages =    116298,
    doi =      {10.1016/j.ssi.2023.116298},
}


@Article{Wan_JColloidInterfaceSci_2023_v648_p317,
    author =   {Xuhao Wan and Zhaofu Zhang and Anyang Wang and Jinhao Su and Wenjun
             Zhou and John Robertson and Yuan Peng and Yu Zheng and Yuzheng Guo},
    title =    {{Deep-learning-assisted theoretical insights into the compatibility of
             environment friendly insulation medium with metal surface of power
             equipment}},
    journal =  {J. Colloid Interface Sci.},
    year =     2023,
    volume =   648,
    pages =    {317--326},
    annote =   {Exploring a new generation of eco-friendly gas insulation medium to
             replace greenhouse gas sulphur hexafluoride (SF6) in power industry is
             significant for reducing the greenhouse effect and building a low-
             carbon environment. The gas-solid compatibility of insulation gas with
             various electrical equipment is also of significance before practical
             applications. Herein, take a promising SF6 replacing gas
             trifluoromethyl sulfonyl fluoride (CF3SO2F) for example, one strategy
             to theoretically evaluate the gas-solid compatibility between
             insulation gas and the typical solid surfaces of common equipment was
             raised. Firstly, the active site where the CF3SO2F molecule is prone
             to interact with other compounds was identified. Secondly, the
             interaction strength and charge transfer between CF3SO2F and four
             typical solid surfaces of equipment were studied by first-principles
             calculations and further analysis was conducted, with SF6 as the
             control group. Then, the dynamic compatibility of CF3SO2F with solid
             surfaces was investigated by large-scale molecular dynamics
             simulations with the aid of deep learning. The results indicate that
             CF3SO2F has excellent compatibility similar to SF6, especially in the
             equipment whose contact surface is Cu, CuO, and Al2O3 due to their
             similar outermost orbital electronic structures. Besides, the dynamic
             compatibility with pure Al surfaces is poor. Finally, preliminary
             experimental verifications indicate the validity of the strategy.},
    doi =      {10.1016/j.jcis.2023.05.188},
}


@Article{Li_JChemPhys_2023_v159_pNone,
    author =   {Zhiqiang Li and Jian Wang and Chao Yang and Linhua Liu and Jia-Yue
             Yang},
    title =    {{Thermal transport across TiO2{\textendash}H2O interface involving
             water dissociation: <i>Ab initio</i>-assisted deep potential molecular
             dynamics}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    14,
    annote =   {Water dissociation on TiO2 surfaces has been known for decades and
             holds great potential in various applications, many of which require a
             proper understanding of thermal transport across the TiO2-H2O
             interface. Molecular dynamics (MD) simulations play an important role
             in characterizing complex systems' interfacial thermal transport
             properties. Nevertheless, due to the imprecision of empirical force
             field potentials, the interfacial thermal transport mechanism
             involving water dissociation remains to be determined. To cope with
             this, a deep potential (DP) model is formulated through the
             utilization of ab{~}initio datasets. This model successfully simulates
             interfacial thermal transport accompanied by water dissociation on the
             TiO2 surfaces. The trained DP achieves a total energy accuracy of
             {\ensuremath{\sim}}238.8{~}meV and a force accuracy of
             {\ensuremath{\sim}}197.05 meV/{\r{A}}. The DPMD simulations show that
             water dissociation induces the formation of hydrogen bonding networks
             and molecular bridges. Structural modifications further affect
             interfacial thermal transport. The interfacial thermal conductance
             estimated by DP is {\ensuremath{\sim}}8.54 {\texttimes} 109 W/m2{~}K,
             smaller than {\ensuremath{\sim}}13.17 {\texttimes} 109 W/m2{~}K by
             empirical potentials. The vibrational density of states (VDOS)
             quantifies the differences between the DP model and empirical
             potentials. Notably, the VDOS disparity between the adsorbed hydrogen
             atoms and normal hydrogen atoms demonstrates the influence of water
             dissociation on heat transfer processes. This work aims to understand
             the effect of water dissociation on thermal transport at the TiO2-H2O
             interface. The findings will provide valuable guidance for the thermal
             management of photocatalytic devices.},
    doi =      {10.1063/5.0167238},
}


@Article{Wisesa_JPhysChemLett_2023_v14_p8741,
    author =   {Pandu Wisesa and Christopher M Andolina and Wissam A Saidi},
    title =    {{Machine-Learning Accelerated First-Principles Accurate Modeling of the
             Solid{\textendash}Liquid Phase Transition in MgO under Mantle
             Conditions}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    39,
    pages =    {8741--8748},
    annote =   {While accurate measurements of MgO under extreme high-pressure
             conditions are needed to understand and model planetary behavior,
             these studies are challenging from both experimental and computational
             modeling perspectives. Herein, we accelerate density functional theory
             (DFT) accurate calculations using deep neural network potentials
             (DNPs) trained over multiple phases and study the melting behavior of
             MgO via the two-phase coexistence (TPC) approach at 0-300 GPa and
             {\ensuremath{\leq}}9600 K. The resulting DNP-TPC melting curve is in
             excellent agreement with existing experimental studies. We show that
             the mitigation of finite-size effects that typically skew the
             predicted melting temperatures in DFT-TPC simulations in excess of
             several hundred kelvin requires models with {\ensuremath{\sim}}16 000
             atoms and >100 ps molecular dynamics trajectories. In addition, the
             DNP can successfully describe MgO metallization well at increased
             pressures that are captured by DFT but missed by classical interatomic
             potentials.},
    doi =      {10.1021/acs.jpclett.3c02424},
}


@Article{Zhang_ProcNatlAcadSciUSA_2023_v120_pe2309952120,
    author =   {Youjun Zhang and Yong Wang and Yuqian Huang and Junjie Wang and Zhixin
             Liang and Long Hao and Zhipeng Gao and Jun Li and Qiang Wu and Hong
             Zhang and Yun Liu and Jian Sun and Jung-Fu Lin},
    title =    {{Collective motion in hcp-Fe at Earth{\textquoteright}s inner core
             conditions}},
    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
    year =     2023,
    volume =   120,
    issue =    41,
    pages =    {e2309952120},
    annote =   {Earth's inner core is predominantly composed of solid iron (Fe) and
             displays intriguing properties such as strong shear softening and an
             ultrahigh Poisson's ratio. Insofar, physical mechanisms to explain
             these features coherently remain highly debated. Here, we have studied
             longitudinal and shear wave velocities of hcp-Fe (hexagonal close-
             packed iron) at relevant pressure-temperature conditions of the inner
             core using in situ shock experiments and machine learning molecular
             dynamics (MLMD) simulations. Our results demonstrate that the shear
             wave velocity of hcp-Fe along the Hugoniot in the premelting
             condition, defined as T/Tm (Tm: melting temperature of iron) above
             0.96, is significantly reduced by ~30%, while Poisson's ratio jumps to
             approximately 0.44. MLMD simulations at 230 to 330 GPa indicate that
             collective motion with fast diffusive atomic migration occurs in
             premelting hcp-Fe primarily along [100] or [010] crystallographic
             direction, contributing to its elastic softening and enhanced
             Poisson's ratio. Our study reveals that hcp-Fe atoms can diffusively
             migrate to neighboring positions, forming open-loop and close-loop
             clusters in the inner core conditions. Hcp-Fe with collective motion
             at the inner core conditions is thus not an ideal solid previously
             believed. The premelting hcp-Fe with collective motion behaves like an
             extremely soft solid with an ultralow shear modulus and an ultrahigh
             Poisson's ratio that are consistent with seismic observations of the
             region. Our findings indicate that premelting hcp-Fe with fast
             diffusive motion represents the underlying physical mechanism to help
             explain the unique seismic and geodynamic features of the inner core.},
    PMCID =    {PMC10576103},
    doi =      {10.1073/pnas.2309952120},
}


@Article{Wang_Unknown_2023_v36_p573,
    author =   {Haidi Wang and Tao Li and Yufan Yao and Xiaofeng Liu and Weiduo Zhu
             and Zhao Chen and Zhongjun Li and Wei Hu},
    title =    {{Atomistic modeling of lithium materials from deep learning potential
             with <b>                      <i>ab initio</i>                   </b>
             accuracy}},
    year =     2023,
    volume =   36,
    issue =    5,
    pages =    {573--581},
    annote =   {<jats:p>Lithium has been paid great attention in recent years thanks
             to its significant applications for battery and lightweight alloy.
             Developing a potential model with high accuracy and efficiency is
             important for theoretical simulation of lithium materials. Here, we
             build a deep learning potential (DP) for elemental lithium based on a
             concurrent-learning scheme and DP representation of the density-
             functional theory (DFT) potential energy surface (PES), the DP model
             enables material simulations with close-to DFT accuracy but at much
             lower computational cost. The simulations show that basic parameters,
             equation of states, elasticity, defects and surface are consistent
             with the first principles results. More notably, the liquid radial
             distribution function based on our DP model is found to match well
             with experiment data. Our results demonstrate that the developed DP
             model can be used for the simulation of lithium materials.</jats:p>},
    doi =      {10.1063/1674-0068/cjcp2211173},
}


@Article{Wu_JPhysChemC_2023_v127_p19115,
    author =   {Chongteng Wu and Tong Liu and Xiayu Ran and Yuefeng Su and Yun Lu and
             Ning Li and Lai Chen and Zhenwei Wu and Feng Wu and Duanyun Cao},
    title =    {{Advancing Accurate and Efficient Surface Behavior Modeling of Al
             Clusters with Machine Learning Potential}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    38,
    pages =    {19115--19126},
    doi =      {10.1021/acs.jpcc.3c03229},
}


@Article{Urata_Unknown_2023_v134_pNone,
    author =   {Shingo Urata and Nobuhiro Nakamura and Junghwan Kim and Hideo Hosono},
    title =    {{Role of hydrogen-doping for compensating oxygen-defect in non-
             stoichiometric amorphous In2O3{\ensuremath{-}}<i>x</i>: Modeling with
             a machine-learning potential}},
    year =     2023,
    volume =   134,
    issue =    11,
    annote =   {<jats:p>Transparent amorphous oxide semiconductors (TAOSs) are
             essential materials and ushering in information and communications
             technologies. The performance of TAOS depends on the microstructures
             relating to the defects and dopants. Density functional theory (DFT)
             is a powerful tool to understand the structure{\textendash}property
             relationship relating to electronic state; however, the computation of
             DFT is expensive, which often hinders appropriate structural modeling
             of amorphous materials. This study, thus, applied machine-learning
             potential (MLP) to reproduce the DFT level of accuracy with enhanced
             efficiency, to model amorphous In2O3 (a-In2O3), instead of expensive
             molecular dynamics (MD) simulations with DFT. MLP-MD could reproduce
             a-In2O3 structure closer to the experimental data in comparison with
             DFT-MD and classical MD simulations with an analytical force field.
             Using the relatively large models obtained by the MLP-MD simulations,
             it was unraveled that the anionic hydrogen atoms bonding to indium
             atoms attract electrons instead of the missing oxygen and remedy the
             optical transparency of the oxygen deficient a-In2O3. The preferential
             formation of metal{\textendash}H bonding through the reaction of
             oxygen vacancy was demonstrated as analogous to InGaZnOx thin films
             [Joonho et al., Appl. Phys. Lett. 110, 232105 (2017)]. The present
             simulation suggests that the same mechanism works in a-In2O3, and our
             finding on the structure{\textendash}property relationship is
             informative to clarify the factors affecting the optical transparency
             of In-based TAOS thin films.</jats:p>},
    doi =      {10.1063/5.0149199},
}


@Article{Zeng_ActaPhysSin_2023_v72_p187102,
    author =   {Qi-Yu Zeng and Bo Chen and Dong-Dong Kang and Jia-Yu Dai},
    title =    {{Large scale and quantum accurate molecular dynamics simulation: Liquid
             iron under extreme condition}},
    journal =  {Acta Phys. Sin.},
    year =     2023,
    volume =   72,
    issue =    18,
    pages =    187102,
    annote =   {<jats:p xml:lang="en">Liquid iron is the major component of planetary
             cores. Its structure and dynamics under high pressure and temperature
             is of great significance in studying geophysics and planetary science.
             However, for experimental techniques, it is still difficult to
             generate and probe such a state of matter under extreme conditions,
             while for theoretical method like molecular dynamics simulation, the
             reliable estimation of dynamic properties requires both large
             simulation size and &lt;i&gt;ab initio&lt;/i&gt; accuracy, resulting
             in unaffordable computational costs for traditional method. Owing to
             the technical limitation, the understanding of such matters remains
             limited. In this work, combining molecular dynamics simulation, we
             establish a neural network potential energy surface model to study the
             static and dynamic properties of liquid iron at its extreme
             thermodynamic state close to core-mantle boundary. The implementation
             of deep neural network extends the simulation scales from one hundred
             atoms to millions of atoms within quantum accuracy. The estimated
             static and dynamic structure factor show good consistency with all
             available X-ray diffraction and inelastic X-ray scattering
             experimental observations, while the empirical potential based on
             embedding-atom-method fails to give a unified description of liquid
             iron across a wide range of thermodynamic conditions. We also
             demonstrate that the transport property like diffusion coefficient
             exhibits a strong size effect, which requires more than at least ten
             thousands of atoms to give a converged value. Our results show that
             the combination of deep learning technology and molecular modelling
             provides a way to describe matter realistically under extreme
             conditions.</jats:p>},
    doi =      {10.7498/aps.72.20231258},
}


@Article{Shen_JAmChemSoc_2023_v145_p20511,
    author =   {Yidi Shen and Sergey I Morozov and Kun Luo and Qi An and William A
             {Goddard Iii}},
    title =    {{Deciphering the Atomistic Mechanism of Si(111)-7 {\texttimes} 7
             Surface Reconstruction Using a Machine-Learning Force Field}},
    journal =  {J. Am. Chem. Soc.},
    year =     2023,
    volume =   145,
    issue =    37,
    pages =    {20511--20520},
    annote =   {While the complex 7 {\texttimes} 7 structure that arises upon
             annealing the Si(111) surface is well-known, the mechanism underlying
             this unusual surface reconstruction has remained a mystery. Here, we
             report molecular dynamics simulations using a machine-learning force
             field for Si to investigate the Si(111)-7 {\texttimes} 7 surface
             reconstruction from the melt. We find that there are two possible
             pathways for the formation of the 7 {\texttimes} 7 structure. The
             first path arises from the growth of a faulted half domain from the
             metastable 5 {\texttimes} 5 phase to the final 7 {\texttimes} 7
             structure, while the second path involves the direct formation of the
             7 {\texttimes} 7 reconstruction. Both pathways involve the creation of
             dimers and bridged five-membered rings, followed by the formation of
             additional dimers and the stabilization of the triangular halves of
             the unit cell. The corner hole is formed from the joining of several
             five-member rings. The insertion of atoms below the adatoms to form a
             dumbbell configuration involves extra atom diffusion or rearrangement
             during the evolution of triangular halves and dimer formation along
             the unit cell boundary. Our findings may provide insights for
             manipulating the surface structure by introducing other atomic
             species.},
    doi =      {10.1021/jacs.3c06540},
}


@Article{Gupta_JMaterChemA_2023_v11_p21864,
    author =   {Mayanak K. Gupta and Sajan Kumar and Ranjan Mittal and Sanjay K.
             Mishra and Stephane Rols and Olivier Delaire and Arumugum Thamizhavel
             and P. U. Sastry and Samrath L. Chaplot},
    title =    {{Distinct anharmonic characteristics of phonon-driven lattice thermal
             conductivity and thermal expansion in bulk MoSe<sub>2</sub> and
             WSe<sub>2</sub>}},
    journal =  {J. Mater. Chem. A},
    year =     2023,
    volume =   11,
    issue =    40,
    pages =    {21864--21873},
    annote =   {<jats:p>Machine-learning molecular dynamics simulations pave the way
             to completely treat the anharmonicity of phonons. Low-energy
             anharmonic modes in transition-metal dichalcogenides drive the thermal
             and transport properties.</jats:p>},
    doi =      {10.1039/d3ta03830k},
}


@Article{Yu_ChemMater_2023_v35_p6651,
    author =   {Wei Yu and Zhaofu Zhang and Xuhao Wan and Jinhao Su and Qingzhong Gui
             and Hailing Guo and Hong-xia Zhong and John Robertson and Yuzheng Guo},
    title =    {{High-Accuracy Machine-Learned Interatomic Potentials for the Phase
             Change Material Ge<sub>3</sub>Sb<sub>6</sub>Te<sub>5</sub>}},
    journal =  {Chem. Mater.},
    year =     2023,
    volume =   35,
    issue =    17,
    pages =    {6651--6658},
    doi =      {10.1021/acs.chemmater.3c00524},
}


@Article{Fu_AdvFunctMaterials_2023_v33_pNone,
    author =   {Fangjia Fu and Xiaoxu Wang and Linfeng Zhang and Yifang Yang and
             Jianhui Chen and Bo Xu and Chuying Ouyang and Shenzhen Xu and Fu{-}Zhi
             Dai and Weinan E},
    title =    {{Unraveling the Atomic{-}scale Mechanism of Phase Transformations and
             Structural Evolutions during (de)Lithiation in Si Anodes}},
    journal =  {Adv Funct Materials},
    year =     2023,
    volume =   33,
    issue =    37,
    annote =   {<jats:title>Abstract</jats:title><jats:p>Unraveling the reaction paths
             and structural evolutions during charging/discharging processes are
             critical for the development and tailoring of silicon anodes for
             high{-}capacity batteries. However, a mechanistic understanding is
             still lacking due to the complex phase transformations between
             crystalline (c{-}) and amorphous (a{-}) phases involved in
             electrochemical cycles. In this study, by employing a newly developed
             machine learning potential, the key experimental phenomena not only
             reproduce, including voltage curves and structural evolution pathways,
             but also provide atomic scale mechanisms associated with these
             phenomena. The voltage plateaus of both the c{-}Si and a{-}Si
             lithiation processes are predicted with the plateau value difference
             close to experimental measurements, revealing the two{-}phase reaction
             mechanism and reaction path differences. The observed voltage
             hysteresis between lithiation and delithiation mainly originates from
             the transformation between the c{-}Li<jats:sub>15{-
             }<jats:italic>{\ensuremath{\delta}}</jats:italic></jats:sub>Si<jats:su
             b>4</jats:sub> and a{-}Li<jats:sub>15{-
             }<jats:italic>{\ensuremath{\delta}}</jats:italic></jats:sub>Si<jats:su
             b>4</jats:sub> phases. Furthermore, stress accumulation is simulated
             along different reaction paths, indicating a better cycling stability
             of the a{-}Si anode due to the lower stress concentration. Overall,
             the study provides a theoretical understanding of the thermodynamics
             of the complex structural evolutions in Si anodes during
             (de)lithiation processes, which may play a role in optimizations for
             battery performances.</jats:p>},
    doi =      {10.1002/adfm.202303936},
}


@Article{Guo_JChemPhys_2023_v159_pNone,
    author =   {Yu-Xin Guo and Yong-Bin Zhuang and Jueli Shi and Jun Cheng},
    title =    {{ChecMatE: A workflow package to automatically generate machine
             learning potentials and phase diagrams for semiconductor alloys}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    9,
    annote =   {Semiconductor alloy materials are highly versatile due to their
             adjustable properties; however, exploring their structural space is a
             challenging task that affects the control of their properties.
             Traditional methods rely on ad{~}hoc design based on the understanding
             of known chemistry and crystallography, which have limitations in
             computational efficiency and search space. In this work, we present
             ChecMatE (Chemical Material Explorer), a software package that
             automatically generates machine learning potentials (MLPs) and uses
             global search algorithms to screen semiconductor alloy materials.
             Taking advantage of MLPs, ChecMatE enables a more efficient and cost-
             effective exploration of the structural space of materials and
             predicts their energy and relative stability with ab{~}initio
             accuracy. We demonstrate the efficacy of ChecMatE through a case study
             of the InxGa1-xN system, where it accelerates structural exploration
             at reduced costs. Our automatic framework offers a promising solution
             to the challenging task of exploring the structural space of
             semiconductor alloy materials.},
    doi =      {10.1063/5.0166858},
}


@Article{Wang_PhysRevMaterials_2023_v7_p093601,
    author =   {Xiao-Yang Wang and Yi-Nan Wang and Ke Xu and Fu-Zhi Dai and Hai-Feng
             Liu and Guang-Hong Lu and Han Wang},
    title =    {{Deep neural network potential for simulating hydrogen blistering in
             tungsten}},
    journal =  {Phys. Rev. Materials},
    year =     2023,
    volume =   7,
    issue =    9,
    pages =    093601,
    doi =      {10.1103/PhysRevMaterials.7.093601},
}


@Article{Yang_NatCatal_2023_v6_p829,
    author =   {Manyi Yang and Umberto Raucci and Michele Parrinello},
    title =    {{Reactant-induced dynamics of lithium imide surfaces during the ammonia
             decomposition process}},
    journal =  {Nat Catal},
    year =     2023,
    volume =   6,
    issue =    9,
    pages =    {829--836},
    doi =      {10.1038/s41929-023-01006-2},
}


@Article{Stoppelman_JChemPhys_2023_v159_pNone,
    author =   {John P Stoppelman and Angus P Wilkinson and Jesse G McDaniel},
    title =    {{Equation of state predictions for ScF3 and CaZrF6 with neural network-
             driven molecular dynamics}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    8,
    annote =   {In silico property prediction based on density functional theory (DFT)
             is increasingly performed for crystalline materials. Whether
             quantitative agreement with experiment can be achieved with current
             methods is often an unresolved question, and may require detailed
             examination of physical effects such as electron correlation,
             reciprocal space sampling, phonon anharmonicity, and nuclear quantum
             effects (NQE), among others. In this work, we attempt first-principles
             equation of state prediction for the crystalline materials ScF3 and
             CaZrF6, which are known to exhibit negative thermal expansion (NTE)
             over a broad temperature range. We develop neural network (NN)
             potentials for both ScF3 and CaZrF6 trained to extensive DFT data, and
             conduct direct molecular dynamics prediction of the equation(s) of
             state over a broad temperature/pressure range. The NN potentials serve
             as surrogates of the DFT Hamiltonian with enhanced computational
             efficiency allowing for simulations with larger supercells and
             inclusion of NQE utilizing path integral approaches. The conclusion of
             the study is mixed: while some equation of state behavior is predicted
             in semiquantitative agreement with experiment, the pressure-induced
             softening phenomenon observed for ScF3 is not captured in our
             simulations. We show that NQE have a moderate effect on NTE at low
             temperature but does not significantly contribute to equation of state
             predictions at increasing temperature. Overall, while the NN
             potentials are valuable for property prediction of these NTE (and
             related) materials, we infer that a higher level of electron
             correlation, beyond the generalized gradient approximation density
             functional employed here, is necessary for achieving quantitative
             agreement with experiment.},
    doi =      {10.1063/5.0157615},
}


@Article{Liu_JChemTheoryComput_2023_v19_p5602,
    author =   {Renxi Liu and Mohan Chen},
    title =    {{Characterization of the Hydrogen-Bond Network in High-Pressure Water
             by Deep Potential Molecular Dynamics}},
    journal =  {J. Chem. Theory Comput.},
    year =     2023,
    volume =   19,
    issue =    16,
    pages =    {5602--5608},
    annote =   {The hydrogen-bond (H-bond) network of high-pressure water is
             investigated by neural-network-based molecular dynamics (MD)
             simulations with first-principles accuracy. The static structure
             factors (SSFs) of water at three densities, i.e., 1, 1.115, and 1.24
             g/cm3, are directly evaluated from 512 water MD trajectories, which
             are in quantitative agreement with the experiments. We propose a new
             method to decompose the computed SSF and identify the changes in the
             SSF with respect to the changes in H-bond structures. We find that a
             larger water density results in a higher probability for one or two
             non-H-bonded water molecules to be inserted into the inner shell,
             explaining the changes in the tetrahedrality of water under pressure.
             We predict that the structure of the accepting end of water molecules
             is more easily influenced by the pressure than by the donating end.
             Our work sheds new light on explaining the SSF and H-bond properties
             in related fields.},
    doi =      {10.1021/acs.jctc.3c00445},
}


@Article{Zhang_PhysRevLett_2023_v131_p076801,
    author =   {Chunyi Zhang and Shuwen Yue and Athanassios Z Panagiotopoulos and
             Michael L Klein and Xifan Wu},
    title =    {{Why Dissolving Salt in Water Decreases Its Dielectric Permittivity}},
    journal =  {Phys. Rev. Lett.},
    year =     2023,
    volume =   131,
    issue =    7,
    pages =    076801,
    annote =   {The dielectric permittivity of salt water decreases on dissolving more
             salt. For nearly a century, this phenomenon has been explained by
             invoking saturation in the dielectric response of the solvent water
             molecules. Herein, we employ an advanced deep neural network (DNN),
             built using data from density functional theory, to study the
             dielectric permittivity of sodium chloride solutions. Notably, the
             decrease in the dielectric permittivity as a function of
             concentration, computed using the DNN approach, agrees well with
             experiments. Detailed analysis of the computations reveals that the
             dominant effect, caused by the intrusion of ionic hydration shells
             into the solvent hydrogen-bond network, is the disruption of dipolar
             correlations among water molecules. Accordingly, the observed decrease
             in the dielectric permittivity is mostly due to increasing suppression
             of the collective response of solvent waters.},
    doi =      {10.1103/PhysRevLett.131.076801},
}


@Article{Zhang_JPhysChemLett_2023_v14_p7141,
    author =   {Jidong Zhang and Wei Guo and Yugui Yao},
    title =    {{Deep Potential Molecular Dynamics Study of Chapman{\textendash}Jouguet
             Detonation Events of Energetic Materials}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    32,
    pages =    {7141--7148},
    annote =   {Detonation of energetic materials (EMs) is of great importance for
             military applications, while the understanding of detailed events and
             mechanisms for the detonation process is scarce. In this study, the
             first deep neural network potential NNP_Shock for molecular dynamics
             (MD) simulation of shock-induced detonation of EMs was generated based
             on a deep potential model, providing DFT accuracy but 106 times the
             computational efficiency. On this basis, we employ our deep potential
             to perform MD simulations of shock-induced detonation of high-
             performance EM material
             2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20,
             C6H6N12O12) and obtain the theoretical Chapman-Jouguet (C-J)
             detonation velocities and pressures directly by multiscale shock
             technique (MSST) for the first time, which are in good agreement with
             experiment. In addition, the Hugoniot curves and initial reaction
             mechanisms were successfully obtained. Therefore, the NNP_Shock
             potential is competent in research of the detonation performance and
             shock sensitivity of CL-20, and the method can also be transplanted to
             studies of other EMs.},
    doi =      {10.1021/acs.jpclett.3c01392},
}


@Article{Sowa_JPhysChemLett_2023_v14_p7215,
    author =   {Jakub K Sowa and Sean T Roberts and Peter J Rossky},
    title =    {{Exploring Configurations of Nanocrystal Ligands Using Machine-Learned
             Force Fields}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    32,
    pages =    {7215--7222},
    annote =   {Semiconducting nanocrystals passivated with organic ligands have
             emerged as a powerful platform for light harvesting, light-driven
             chemical reactions, and sensing. Due to their complexity and size,
             little structural information is available from experiments, making
             these systems challenging to model computationally. Here, we develop a
             machine-learned force field trained on DFT data and use it to
             investigate the surface chemistry of a PbS nanocrystal interfaced with
             acetate ligands. In doing so, we go beyond considering individual
             local minimum energy geometries and, importantly, circumvent a
             precarious issue associated with the assumption of a single assigned
             atomic partial charge for each element in a nanocrystal, independent
             of its structural position. We demonstrate that the carboxylate
             ligands passivate the metal-rich surfaces by adopting a very wide
             range of "tilted-bridge" and "bridge" geometries and investigate the
             corresponding ligand IR spectrum. This work illustrates the potential
             of machine-learned force fields to transform computational modeling of
             these materials.},
    doi =      {10.1021/acs.jpclett.3c01618},
}


@Article{Chtchelkatchev_JChemPhys_2023_v159_pNone,
    author =   {N M Chtchelkatchev and R E Ryltsev and M V Magnitskaya and S M
             Gorbunov and K A Cherednichenko and V L Solozhenko and V V Brazhkin},
    title =    {{Local structure, thermodynamics, and melting of boron phosphide at
             high pressures by deep learning-driven <i>ab{~}initio</i> simulations}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    6,
    annote =   {Boron phosphide (BP) is a (super)hard semiconductor constituted of
             light elements, which is promising for high demand applications at
             extreme conditions. The behavior of BP at high temperatures and
             pressures is of special interest but is also poorly understood because
             both experimental and conventional ab{~}initio methods are restricted
             to studying refractory covalent materials. The use of machine learning
             interatomic potentials is a revolutionary trend that gives a unique
             opportunity for high-temperature study of materials with ab{~}initio
             accuracy. We develop a deep machine learning potential (DP) for
             accurate atomistic simulations of the solid and liquid phases of BP as
             well as their transformations near the melting line. Our DP provides
             quantitative agreement with experimental and ab{~}initio molecular
             dynamics data for structural and dynamic properties. DP-based
             simulations reveal that at ambient pressure, a tetrahedrally bonded
             cubic BP crystal melts into an open structure consisting of two
             interpenetrating sub-networks of boron and phosphorous with different
             structures. Structure transformations of BP melt under compressing are
             reflected by the evolution of low-pressure tetrahedral coordination to
             high-pressure octahedral coordination. The main contributions to
             structural changes at low pressures are made by the evolution of
             medium-range order in the B-subnetwork and, at high pressures, by the
             change of short-range order in the P-subnetwork. Such transformations
             exhibit an anomalous behavior of structural characteristics in the
             range of 12-15{~}GPa. DP-based simulations reveal that the Tm(P) curve
             develops a maximum at P {\ensuremath{\approx}} 13 GPa, whereas
             experimental studies provide two separate branches of the melting
             curve, which demonstrate the opposite behavior. Analysis of the
             results obtained raises open issues in developing machine learning
             potentials for covalent materials and stimulates further experimental
             and theoretical studies of melting behavior in BP.},
    doi =      {10.1063/5.0165948},
}


@Article{Zhang_JPhysChemB_2023_v127_p7011,
    author =   {Cunzhi Zhang and Marcello Puligheddu and Linfeng Zhang and Roberto Car
             and Giulia Galli},
    title =    {{Thermal Conductivity of Water at Extreme Conditions}},
    journal =  {J. Phys. Chem. B},
    year =     2023,
    volume =   127,
    issue =    31,
    pages =    {7011--7},
    annote =   {Measuring the thermal conductivity ({\ensuremath{\kappa}}) of water at
             extreme conditions is a challenging task, and few experimental data
             are available. We predict {\ensuremath{\kappa}} for temperatures and
             pressures relevant to the conditions of the Earth mantle, between
             1,000 and 2,000 K and up to 22 GPa. We employ close to equilibrium
             molecular dynamics simulations and a deep neural network potential
             fitted to density functional theory data. We then interpret our
             results by computing the equation of state of water on a fine grid of
             points and using a simple model for {\ensuremath{\kappa}}. We find
             that the thermal conductivity is weakly dependent on temperature and
             monotonically increases with pressure with an approximate square-root
             behavior. In addition, we show how the increase of
             {\ensuremath{\kappa}} at high pressure, relative to ambient
             conditions, is related to the corresponding increase in the sound
             velocity. Although the relationships between the thermal conductivity,
             pressure and sound velocity established here are not rigorous, they
             are sufficiently accurate to allow for a robust estimate of the
             thermal conductivity of water in a broad range of temperatures and
             pressures, where experiments are still difficult to perform.},
    PMCID =    {PMC10424233},
    doi =      {10.1021/acs.jpcb.3c02972},
}


@Article{Tuo_AdvFunctMaterials_2023_v33_pNone,
    author =   {Ping Tuo and Lei Li and Xiaoxu Wang and Jianhui Chen and Zhicheng
             Zhong and Bo Xu and Fu{-}Zhi Dai},
    title =    {{Spontaneous Hybrid Nano{-}Domain Behavior of the
             Organic{\textendash}Inorganic Hybrid Perovskites}},
    journal =  {Adv Funct Materials},
    year =     2023,
    volume =   33,
    issue =    32,
    annote =   {<jats:title>Abstract</jats:title><jats:p>In hybrid perovskites, the
             organic molecules and inorganic frameworks exhibit distinct static and
             dynamic characteristics. Their coupling will lead to fascinating
             phenomena, such as large polarons, dynamic
             Rashba{\textendash}Dresselhaus effects, etc. In this paper, deep
             potential molecular dynamics (DPMD) is employed, a large{-}scale MD
             simulation scheme with DFT accuracy, to study hybrid perovskites
             formamidinium lead iodide (FAPbI<jats:sub>3</jats:sub>) and
             methylamonium lead iodide (MAPbI<jats:sub>3</jats:sub>). A spontaneous
             hybrid nano{-}domain behavior, namely multiple molecular rotation
             nano{-}domains embedded into a single
             [PbI<jats:sub>6</jats:sub>]<jats:sup>4{\ensuremath{-}}</jats:sup>
             octahedra rotation domain, is first discovered at low temperatures.
             The behavior originates from the interplay between the long range
             order of molecular rotation and local lattice deformation, and
             clarifies the puzzling structural features of
             FAPbI<jats:sub>3</jats:sub> at low temperatures. The work provides new
             insights into the structural characteristics and stability of hybrid
             perovskite, as well as new ideas for the structural characterization
             of organic{\textendash}inorganic coupled{~}systems.</jats:p>},
    doi =      {10.1002/adfm.202301663},
}


@Article{Piaggi_JChemPhys_2023_v159_pNone,
    author =   {Pablo M Piaggi and Thomas E Gartner and Roberto Car and Pablo G
             Debenedetti},
    title =    {{Melting curves of ice polymorphs in the vicinity of the
             liquid{\textendash}liquid critical point}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    5,
    annote =   {The possible existence of a liquid-liquid critical point in deeply
             supercooled water has been a subject of debate due to the challenges
             associated with providing definitive experimental evidence. The
             pioneering work by Mishima and Stanley [Nature 392, 164-168 (1998)]
             sought to shed light on this problem by studying the melting curves of
             different ice polymorphs and their metastable continuation in the
             vicinity of the expected liquid-liquid transition and its associated
             critical point. Based on the continuous or discontinuous changes in
             the slope of the melting curves, Mishima [Phys. Rev. Lett. 85, 334
             (2000)] suggested that the liquid-liquid critical point lies between
             the melting curves of ice III and ice V. We explore this conjecture
             using molecular dynamics simulations with a machine learning model
             based on ab{~}initio quantum-mechanical calculations. We study the
             melting curves of ices III, IV, V, VI, and XIII and find that all of
             them are supercritical and do not intersect the liquid-liquid
             transition locus. We also find a pronounced, yet continuous, change in
             the slope of the melting lines upon crossing of the liquid locus of
             maximum compressibility. Finally, we analyze the literature in light
             of our findings and conclude that the scenario in which the melting
             curves are supercritical is favored by the most recent computational
             and experimental evidence. Although the preponderance of evidence is
             consistent with the existence of a second critical point in water, the
             behavior of ice polymorph melting lines does not provide strong
             evidence in support of this viewpoint, according to our calculations.},
    doi =      {10.1063/5.0159288},
}


@Article{Zeng_JChemPhys_2023_v159_pNone,
    author =   {Jinzhe Zeng and Duo Zhang and Denghui Lu and Pinghui Mo and Zeyu Li
             and Yixiao Chen and Mari{\'a}n Rynik and Li'ang Huang and Ziyao Li and
             Shaochen Shi and Yingze Wang and Haotian Ye and Ping Tuo and Jiabin
             Yang and Ye Ding and Yifan Li and Davide Tisi and Qiyu Zeng and Han
             Bao and Yu Xia and Jiameng Huang and Koki Muraoka and Yibo Wang and
             Junhan Chang and Fengbo Yuan and Sigbj{\o}rn L{\o}land Bore and Chun
             Cai and Yinnian Lin and Bo Wang and Jiayan Xu and Jia-Xin Zhu and
             Chenxing Luo and Yuzhi Zhang and Rhys E A Goodall and Wenshuo Liang
             and Anurag Kumar Singh and Sikai Yao and Jingchao Zhang and Renata
             Wentzcovitch and Jiequn Han and Jie Liu and Weile Jia and Darrin M
             York and Weinan E and Roberto Car and Linfeng Zhang and Han Wang},
    title =    {{DeePMD-kit v2: A software package for deep potential models}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    5,
    annote =   {DeePMD-kit is a powerful open-source software package that facilitates
             molecular dynamics simulations using machine learning potentials known
             as Deep Potential (DP) models. This package, which was released in
             2017, has been widely used in the fields of physics, chemistry,
             biology, and material science for studying atomistic systems. The
             current version of DeePMD-kit offers numerous advanced features, such
             as DeepPot-SE, attention-based and hybrid descriptors, the ability to
             fit tensile properties, type embedding, model deviation, DP-range
             correction, DP long range, graphics processing unit support for
             customized operators, model compression, non-von Neumann molecular
             dynamics, and improved usability, including documentation, compiled
             binary packages, graphical user interfaces, and application
             programming interfaces. This article presents an overview of the
             current major version of the DeePMD-kit package, highlighting its
             features and technical details. Additionally, this article presents a
             comprehensive procedure for conducting molecular dynamics as a
             representative application, benchmarks the accuracy and efficiency of
             different models, and discusses ongoing developments.},
    PMCID =    {PMC10445636},
    doi =      {10.1063/5.0155600},
}


@Article{Hou_AngewChemIntEdEngl_2023_v62_pe202304205,
    author =   {Pengfei Hou and Yumiao Tian and Yu Xie and Fei Du and Gang Chen and
             Aleksandra Vojvodic and Jianzhong Wu and Xing Meng},
    title =    {{Unraveling the Oxidation Behaviors of MXenes in Aqueous Systems by
             Active{-}Learning{-}Potential Molecular{-}Dynamics Simulation}},
    journal =  {Angew. Chem. Int. Ed. Engl.},
    year =     2023,
    volume =   62,
    issue =    32,
    pages =    {e202304205},
    annote =   {MXenes are 2D materials with great potential in various applications.
             However, the degradation of MXenes in humid environments has become a
             main obstacle in their practical use. Here we combine deep neural
             networks and an active learning scheme to develop a neural network
             potential (NNP) for aqueous MXene systems with ab initio precision but
             low cost. The oxidation behaviors of super large aqueous MXene systems
             are investigated systematically at nanosecond timescales for the first
             time. The oxidation process of MXenes is clearly displayed at the
             atomic level. Free protons and oxides greatly inhibit subsequent
             oxidation reactions, leading to the degree of oxidation of MXenes to
             exponentially decay with time, which is consistent with the oxidation
             rate of MXenes measured experimentally. Importantly, this
             computational study represents the first exploration of the kinetic
             process of oxidation of super-sized aqueous MXene systems. It opens a
             promising avenue for the future development of effective protection
             strategies aimed at controlling the stability of MXenes.},
    doi =      {10.1002/anie.202304205},
}


@Article{Andolina_DigitalDiscovery_2023_v2_p1070,
    author =   {Christopher M. Andolina and Wissam A. Saidi},
    title =    {{Highly transferable atomistic machine-learning potentials from curated
             and compact datasets across the periodic table}},
    journal =  {Digital Discovery},
    year =     2023,
    volume =   2,
    issue =    4,
    pages =    {1070--1077},
    annote =   {<jats:p>Machine learning atomistic potentials (MLPs) trained using
             density functional theory (DFT) datasets allow for the modeling of
             complex material properties with near-DFT accuracy while imposing a
             fraction of its computational cost.</jats:p>},
    doi =      {10.1039/d3dd00046j},
}


@Article{Ren_NatMater_2023_v22_p999,
    author =   {Qingyong Ren and Mayanak K Gupta and Min Jin and Jingxuan Ding and
             Jiangtao Wu and Zhiwei Chen and Siqi Lin and Oscar Fabelo and Jose
             Alberto Rodr{\'\i}guez-Velamaz{\'a}n and Maiko Kofu and Kenji Nakajima
             and Marcell Wolf and Fengfeng Zhu and Jianli Wang and Zhenxiang Cheng
             and Guohua Wang and Xin Tong and Yanzhong Pei and Olivier Delaire and
             Jie Ma},
    title =    {{Extreme phonon anharmonicity underpins superionic diffusion and
             ultralow thermal conductivity in argyrodite Ag8SnSe6}},
    journal =  {Nat. Mater.},
    year =     2023,
    volume =   22,
    issue =    8,
    pages =    {999--1006},
    annote =   {Ultralow thermal conductivity and fast ionic diffusion endow
             superionic materials with excellent performance both as thermoelectric
             converters and as solid-state electrolytes. Yet the correlation and
             interdependence between these two features remain unclear owing to a
             limited understanding of their complex atomic dynamics. Here we
             investigate ionic diffusion and lattice dynamics in argyrodite
             Ag8SnSe6 using synchrotron X-ray and neutron scattering techniques
             along with machine-learned molecular dynamics. We identify a critical
             interplay of the vibrational dynamics of mobile Ag and a host
             framework that controls the overdamping of low-energy Ag-dominated
             phonons into a quasi-elastic response, enabling superionicity.
             Concomitantly, the persistence of long-wavelength transverse acoustic
             phonons across the superionic transition challenges a proposed
             'liquid-like thermal conduction' picture. Rather, a striking thermal
             broadening of low-energy phonons, starting even below 50{\,}K, reveals
             extreme phonon anharmonicity and weak bonding as underlying features
             of the potential energy surface responsible for the ultralow thermal
             conductivity (<0.5{\,}W{\,}m-1{\,}K-1) and fast diffusion. Our results
             provide fundamental insights into the complex atomic dynamics in
             superionic materials for energy conversion and storage.},
    doi =      {10.1038/s41563-023-01560-x},
}


@Article{Xiao_Unknown_2023_v123_pNone,
    author =   {R. L. Xiao and K. L. Liu and Y. Ruan and B. Wei},
    title =    {{Rapid acquisition of liquid thermophysical properties from pure metals
             to quaternary alloys by proposing a machine learning strategy}},
    year =     2023,
    volume =   123,
    issue =    5,
    annote =   {<jats:p>The establishment of reliable materials genome databases
             involving the thermophysical properties of liquid metals and alloys
             promotes the progress of materials research and development, whereas
             acquiring these properties imposes great challenges on experimental
             investigation. Here, we proposed a deep learning method and achieved a
             deep neural network (DNN) interatomic potential for the entire
             Ti{\textendash}Ni{\textendash}Cr{\textendash}Al system from pure
             metals to quaternary alloys. This DNN potential exhibited sufficient
             temperature and compositional transformability which extended beyond
             the training and provided the prediction of the liquid structure and
             thermophysical properties for metallic materials with both density
             functional theory accuracy and classic molecular dynamics efficiency.
             The predicted results agreed well with the reported experimental data.
             This work opens a feasible way to address the challenges of rapidly
             and accurately acquiring thermophysical properties data for liquid
             pure metals and multicomponent alloys, covering a broad temperature
             range from superheated to undercooled state.</jats:p>},
    doi =      {10.1063/5.0160046},
}


@Article{Liu_JChemPhys_2023_v159_pNone_2,
    author =   {Dongfei Liu and Jianzhong Wu and Diannan Lu},
    title =    {{Transferability evaluation of the deep potential model for simulating
             water-graphene confined system}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    4,
    annote =   {Machine learning potentials (MLPs) are poised to combine the accuracy
             of ab{~}initio predictions with the computational efficiency of
             classical molecular dynamics (MD) simulation. While great progress has
             been made over the last two decades in developing MLPs, there is still
             much to be done to evaluate their model transferability and facilitate
             their development. In this work, we construct two deep potential (DP)
             models for liquid water near graphene surfaces, Model S and Model F,
             with the latter having more training data. A concurrent learning
             algorithm (DP-GEN) is adopted to explore the configurational space
             beyond the scope of conventional ab{~}initio MD simulation. By
             examining the performance of Model S, we find that an accurate
             prediction of atomic force does not imply an accurate prediction of
             system energy. The deviation from the relative atomic force alone is
             insufficient to assess the accuracy of the DP models. Based on the
             performance of Model F, we propose that the relative magnitude of the
             model deviation and the corresponding root-mean-square error of the
             original test dataset, including energy and atomic force, can serve as
             an indicator for evaluating the accuracy of the model prediction for a
             given structure, which is particularly applicable for large systems
             where density functional theory calculations are infeasible. In
             addition to the prediction accuracy of the model described above, we
             also briefly discuss simulation stability and its relationship to the
             former. Both are important aspects in assessing the transferability of
             the MLP model.},
    doi =      {10.1063/5.0153196},
}


@Article{Deng_ACSNano_2023_v17_p14099,
    author =   {Yuanpeng Deng and Shubin Fu and Jingran Guo and Xiang Xu and Hui Li},
    title =    {{Anisotropic Collective Variables with Machine Learning Potential for
             <i>Ab Initio</i> Crystallization of Complex Ceramics}},
    journal =  {ACS Nano},
    year =     2023,
    volume =   17,
    issue =    14,
    pages =    {14099--14113},
    annote =   {Enhanced sampling molecular dynamics (MD) simulations have been
             extensively used in the phase transition study of simple crystalline
             materials, such as aluminum, silica, and ice. However, MD simulation
             of the crystallization process for complex crystalline materials still
             faces a formidable challenge due to their multicomponent induced
             multiphase problem. Here, we realize the ab initio accuracy MD
             crystallization simulations of complex ceramics by using anisotropic
             collective variables (CVs) and machine learning (ML) potential. The
             anisotropic X-ray diffraction intensity CVs provide precise
             identification of complex crystal structures with detailed
             crystallography information, while the ML potential makes it feasible
             to further perform enhanced sampling simulations with ab initio
             accuracy. We verify the universality and accuracy of this method
             through complex ceramics with three kinds of representative
             structures, i.e., Ti3SiC2 for the MAX structure, zircon for the
             mineral structure, and lead zirconate titanate for the perovskite
             structure. It demonstrates exceptional efficiency and ab initio
             quality in achieving crystallization and generating free energy
             surfaces of all these ceramics, facilitating the analysis and design
             of complex crystalline materials.},
    doi =      {10.1021/acsnano.3c04602},
}


@Article{Crippa_ProcNatlAcadSciUSA_2023_v120_pe2300565120,
    author =   {Martina Crippa and Annalisa Cardellini and Cristina Caruso and
             Giovanni M Pavan},
    title =    {{Detecting dynamic domains and local fluctuations in complex molecular
             systems via timelapse neighbors shuffling}},
    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
    year =     2023,
    volume =   120,
    issue =    30,
    pages =    {e2300565120},
    annote =   {It is known that the behavior of many complex systems is controlled by
             local dynamic rearrangements or fluctuations occurring within them.
             Complex molecular systems, composed of many molecules interacting with
             each other in a Brownian storm, make no exception. Despite the rise of
             machine learning and of sophisticated structural descriptors,
             detecting local fluctuations and collective transitions in complex
             dynamic ensembles remains often difficult. Here, we show a machine
             learning framework based on a descriptor which we name Local
             Environments and Neighbors Shuffling (LENS), that allows identifying
             dynamic domains and detecting local fluctuations in a variety of
             systems in an abstract and efficient way. By tracking how much the
             microscopic surrounding of each molecular unit changes over time in
             terms of neighbor individuals, LENS allows characterizing the global
             (macroscopic) dynamics of molecular systems in phase transition,
             phases-coexistence, as well as intrinsically characterized by local
             fluctuations (e.g., defects). Statistical analysis of the LENS time
             series data extracted from molecular dynamics trajectories of, for
             example, liquid-like, solid-like, or dynamically diverse complex
             molecular systems allows tracking in an efficient way the presence of
             different dynamic domains and of local fluctuations emerging within
             them. The approach is found robust, versatile, and applicable
             independently of the features of the system and simply provided that a
             trajectory containing information on the relative motion of the
             interacting units is available. We envisage that "such a LENS" will
             constitute a precious basis for exploring the dynamic complexity of a
             variety of systems and, given its abstract definition, not necessarily
             of molecular ones.},
    PMCID =    {PMC10372573},
    doi =      {10.1073/pnas.2300565120},
}


@Article{Guo_JChemPhys_2023_v159_pNone_2,
    author =   {Longfei Guo and Tao Jin and Shuang Shan and Quan Tang and Zhen Li and
             Chongyang Wang and Junpeng Wang and Bowei Pan and Qiao Wang and Fuyi
             Chen},
    title =    {{Structural transformations in single-crystalline AgPd nanoalloys from
             multiscale deep potential molecular dynamics}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    2,
    annote =   {AgPd nanoalloys often undergo structural evolution during catalytic
             reactions; the mechanism underlying such restructuring remains largely
             unknown due to the use of oversimplified interatomic potentials in
             simulations. Herein, a deep-learning potential is developed for AgPd
             nanoalloys based on a multiscale dataset spanning from nanoclusters to
             bulk configurations, exhibits precise predictions of mechanical
             properties and formation energies with near-density functional theory
             accuracy, calculates the surface energies closer to experimental
             values compared to those obtained by Gupta potentials, and is applied
             to investigate the shape reconstruction of single-crystalline AgPd
             nanoalloys from cuboctahedron (Oh) to icosahedron (Ih) geometries. The
             Oh to Ih shape restructuring is thermodynamically favorable and occurs
             at 11 and 92{~}ps for Pd55 \at Ag254 and Ag147 \at Pd162 nanoalloys,
             respectively. During the shape reconstruction of Pd \at Ag nanoalloys,
             concurrent surface restructuring of the (100) facet and internal
             multi-twinned phase change are observed with collaborative displacive
             characters. The presence of vacancies can influence the final product
             and reconstructing rate of Pd \at Ag core-shell nanoalloys. The Ag outward
             diffusion on Ag \at Pd nanoalloys is more pronounced in Ih geometry
             compared to Oh geometry and can be further accelerated by the Oh to Ih
             deformation. The deformation of single-crystalline Pd \at Ag nanoalloys is
             characterized by a displacive transformation involving the
             collaborative displacement of a large number of atoms, distinguishing
             it from the diffusion-coupled transformation of Ag \at Pd nanoalloys.},
    doi =      {10.1063/5.0158918},
}


@Article{Liu_JChemPhys_2023_v159_pNone,
    author =   {Da-Jiang Liu and James W Evans},
    title =    {{Fluorine spillover for ceria- vs silica-supported palladium
             nanoparticles: A MD study using machine learning potentials}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    2,
    annote =   {Supported metallic nanoparticles play a central role in catalysis.
             However, predictive modeling is particularly challenging due to the
             structural and dynamic complexity of the nanoparticle and its
             interface with the support, given that the sizes of interest are often
             well beyond those accessible via traditional ab{~}initio methods. With
             recent advances in machine learning, it is now feasible to perform MD
             simulations with potentials retaining near-density-functional theory
             (DFT) accuracy, which can elucidate the growth and relaxation of
             supported metal nanoparticles, as well as reactions on those
             catalysts, at temperatures and time scales approaching those relevant
             to experiments. Furthermore, the surfaces of the support materials can
             also be modeled realistically through simulated annealing to include
             effects such as defects and amorphous structures. We study the
             adsorption of fluorine atoms on ceria and silica supported palladium
             nanoparticles using machine learning potential trained by DFT data
             using the DeePMD framework. We show defects on ceria and Pd/ceria
             interfaces are crucial for the initial adsorption of fluorine, while
             the interplay between Pd and ceria and the reverse oxygen migration
             from ceria to Pd control spillover of fluorine from Pd to ceria at
             later stages. In contrast, silica supports do not induce fluorine
             spillover from Pd particles.},
    doi =      {10.1063/5.0147132},
}


@Article{Ding_JChemPhys_2023_v159_pNone,
    author =   {Zhutian Ding and Annabella Selloni},
    title =    {{Modeling the aqueous interface of amorphous TiO2 using deep potential
             molecular dynamics}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    issue =    2,
    annote =   {Amorphous titanium dioxide (a-TiO2) is widely used as a coating
             material in applications such as electrochemistry and self-cleaning
             surfaces where its interface with water has a central role. However,
             little is known about the structures of the a-TiO2 surface and aqueous
             interface, particularly at the microscopic level. In this work, we
             construct a model of the a-TiO2 surface via a cut-melt-and-quench
             procedure based on molecular dynamics simulations with deep neural
             network potentials (DPs) trained on density functional theory data.
             After interfacing the a-TiO2 surface with water, we investigate the
             structure and dynamics of the resulting system using a combination of
             DP-based molecular dynamics (DPMD) and ab{~}initio molecular dynamics
             (AIMD) simulations. Both AIMD and DPMD simulations reveal that the
             distribution of water on the a-TiO2 surface lacks distinct layers
             normally found at the aqueous interface of crystalline TiO2, leading
             to an {\ensuremath{\sim}}10 times faster diffusion of water at the
             interface. Bridging hydroxyls (Ti2-ObH) resulting from water
             dissociation decay several times more slowly than terminal hydroxyls
             (Ti-OwH) due to fast Ti-OwH2 {\textrightarrow} Ti-OwH proton exchange
             events. These results provide a basis for a detailed understanding of
             the properties of a-TiO2 in electrochemical environments. Moreover,
             the procedure of generating the a-TiO2-interface employed here is
             generally applicable to studying the aqueous interfaces of amorphous
             metal oxides.},
    doi =      {10.1063/5.0157188},
}


@Article{Xie_JPhysChemC_2023_v127_p13228,
    author =   {Jun-Zhong Xie and Hong Jiang},
    title =    {{Revealing Carbon Vacancy Distribution on
             {\ensuremath{\alpha}}-MoC<sub>1{\textendash}<i>x</i></sub> Surfaces by
             Machine-Learning Force-Field-Aided Cluster Expansion Approach}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    27,
    pages =    {13228--13237},
    doi =      {10.1021/acs.jpcc.3c01941},
}


@Article{Huo_JChemTheoryComput_2023_v19_p4243,
    author =   {Jun Huo and Jianghao Chen and Pei Liu and Benkun Hong and Jian Zhang
             and Hao Dong and Shuhua Li},
    title =    {{Microscopic Mechanism of Proton Transfer in Pure Water under Ambient
             Conditions}},
    journal =  {J. Chem. Theory Comput.},
    year =     2023,
    volume =   19,
    issue =    13,
    pages =    {4243--4254},
    annote =   {Water molecules and the associated proton transfer (PT) are prevalent
             in chemical and biological systems and have been a hot research topic.
             Spectroscopic characterization and ab initio molecular dynamics (AIMD)
             simulations have previously revealed insights into acidic and basic
             liquids. Presumably, the situation in the acidic/basic solution is not
             necessarily the same as in pure water; in addition, the autoionization
             constant for water is only 10-14 under ambient conditions, making the
             study of PT in pure water challenging. To overcome this issue, we
             modeled periodic water box systems containing 1000 molecules for tens
             of nanoseconds based on a neural network potential (NNP) with quantum
             mechanical accuracy. The NNP was generated by training a dataset
             containing the energies and atomic forces of 17 075 configurations of
             periodic water box systems, and these data points were calculated at
             the MP2 level that considers electron correlation effects. We found
             that the size of the system and the duration of the simulation have a
             significant impact on the convergence of the results. With these
             factors considered, our simulations showed that hydronium (H3O+) and
             hydroxide (OH-) ions in water have distinct hydration structures,
             thermodynamic and kinetic properties, e.g., the longer-lasting and
             more stable hydrated structure of OH- ions than that of H3O+, as well
             as a significantly higher free energy barrier for the OH--associated
             PT than that of H3O+, leading the two to exhibit completely different
             PT behaviors. Given these characteristics, we further found that PT
             via OH- ions tends not to occur multiple times or between many
             molecules. In contrast, PT via H3O+ can synergistically occur among
             multiple molecules and prefers to adopt a cyclic pattern among three
             water molecules, while it occurs mostly in a chain pattern when more
             water molecules are involved. Therefore, our studies provide a
             detailed and solid microscopic explanation for the PT process in pure
             water.},
    doi =      {10.1021/acs.jctc.3c00244},
}


@Article{Ko_JChemTheoryComput_2023_v19_p4182,
    author =   {Hsin-Yu Ko and Marcos F {Calegari Andrade} and Zachary M Sparrow and
             Ju-An Zhang and Robert A {DiStasio Jr}},
    title =    {{High-Throughput Condensed-Phase Hybrid Density Functional Theory for
             Large-Scale Finite-Gap Systems: The <tt>SeA</tt> Approach}},
    journal =  {J. Chem. Theory Comput.},
    year =     2023,
    volume =   19,
    issue =    13,
    pages =    {4182--4201},
    annote =   {High-throughput electronic structure calculations (often performed
             using density functional theory (DFT)) play a central role in
             screening existing and novel materials, sampling potential energy
             surfaces, and generating data for machine learning applications. By
             including a fraction of exact exchange (EXX), hybrid functionals
             reduce the self-interaction error in semilocal DFT and furnish a more
             accurate description of the underlying electronic structure, albeit at
             a computational cost that often prohibits such high-throughput
             applications. To address this challenge, we have constructed a robust,
             accurate, and computationally efficient framework for high-throughput
             condensed-phase hybrid DFT and implemented this approach in the PWSCF
             module of Quantum ESPRESSO (QE). The resulting SeA approach (SeA =
             SCDM + exx + ACE) combines and seamlessly integrates: (i) the selected
             columns of the density matrix method (SCDM, a robust noniterative
             orbital localization scheme that sidesteps system-dependent
             optimization protocols), (ii) a recently extended version of exx (a
             black-box linear-scaling EXX algorithm that exploits sparsity between
             localized orbitals in real space when evaluating the action of the
             standard/full-rank V^xx operator), and (iii) adaptively compressed
             exchange (ACE, a low-rank V^xx approximation). In doing so, SeA
             harnesses three levels of computational savings: pair selection and
             domain truncation from SCDM + exx (which only considers spatially
             overlapping orbitals on orbital-pair-specific and system-size-
             independent domains) and low-rank V^xx approximation from ACE (which
             reduces the number of calls to SCDM + exx during the self-consistent
             field (SCF) procedure). Across a diverse set of 200 nonequilibrium
             (H2O)64 configurations (with densities spanning 0.4-1.7 g/cm3), SeA
             provides a 1-2 order-of-magnitude speedup in the overall time-to-
             solution, i.e., {\ensuremath{\approx}}8-26{\texttimes} compared to the
             convolution-based PWSCF(ACE) implementation in QE and
             {\ensuremath{\approx}}78-247{\texttimes} compared to the conventional
             PWSCF(Full) approach, and yields energies, ionic forces, and other
             properties with high fidelity. As a proof-of-principle high-throughput
             application, we trained a deep neural network (DNN) potential for
             ambient liquid water at the hybrid DFT level using SeA via an actively
             learned data set with {\ensuremath{\approx}}8,700 (H2O)64
             configurations. Using an out-of-sample set of (H2O)512 configurations
             (at nonambient conditions), we confirmed the accuracy of this SeA-
             trained potential and showcased the capabilities of SeA by computing
             the ground-truth ionic forces in this challenging system containing
             >1,500 atoms.},
    doi =      {10.1021/acs.jctc.2c00827},
}


@Article{Ran_JPhysChemLett_2023_v14_p6028,
    author =   {Jingyi Ran and Bipeng Wang and Yifan Wu and Dongyu Liu and Carlos
             {Mora Perez} and Andrey S Vasenko and Oleg V Prezhdo},
    title =    {{Halide Vacancies Create No Charge Traps on Lead Halide Perovskite
             Surfaces but Can Generate Deep Traps in the Bulk}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    26,
    pages =    {6028--6036},
    annote =   {Metal halide perovskites (MHPs) have attracted attention because of
             their high optoelectronic performance that is fundamentally rooted in
             the unusual properties of MHP defects. By developing an ab initio-
             based machine-learning force field, we sample the structural dynamics
             of MHPs on a nanosecond time scale and show that halide vacancies
             create midgap trap states in the MHP bulk but not on a surface. Deep
             traps result from Pb-Pb dimers that can form across the vacancy in
             only the bulk. The required shortening of the Pb-Pb distance by nearly
             3 {\r{A}} is facilitated by either charge trapping or 50 ps thermal
             fluctuations. The large-scale structural deformations are possible
             because MHPs are soft. Halide vacancies on the MHP surface create no
             deep traps but separate electrons from holes, keeping the charges
             mobile. This is particularly favorable for MHP quantum dots, which do
             not require sophisticated surface passivation to emit light and blink
             less than quantum dots formed from traditional inorganic
             semiconductors.},
    doi =      {10.1021/acs.jpclett.3c01231},
}


@Article{Wen_InternationalJournalofPlasticity_2023_v166_p103644,
    author =   {Tongqi Wen and Anwen Liu and Rui Wang and Linfeng Zhang and Jian Han
             and Han Wang and David J. Srolovitz and Zhaoxuan Wu},
    title =    {{Modelling of dislocations, twins and crack-tips in HCP and BCC Ti}},
    journal =  {International Journal of Plasticity},
    year =     2023,
    volume =   166,
    pages =    103644,
    doi =      {10.1016/j.ijplas.2023.103644},
}


@Article{Fan_JournalofEnergyChemistry_2023_v82_p239,
    author =   {Xue-Ting Fan and Xiao-Jian Wen and Yong-Bin Zhuang and Jun Cheng},
    title =    {{Molecular insight into the GaP(110)-water interface using machine
             learning accelerated molecular dynamics}},
    journal =  {Journal of Energy Chemistry},
    year =     2023,
    volume =   82,
    pages =    {239--247},
    doi =      {10.1016/j.jechem.2023.03.013},
}


@Article{Qu_JElectronMater_2023_v52_p4475,
    author =   {Ruijin Qu and Yawei Lv and Zhihong Lu},
    title =    {{A Deep Neural Network Potential to Study the Thermal Conductivity of
             MnBi2Te4 and Bi2Te3/MnBi2Te4 Superlattice}},
    journal =  {J. Electron. Mater.},
    year =     2023,
    volume =   52,
    issue =    7,
    pages =    {4475--4483},
    doi =      {10.1007/s11664-023-10403-z},
}


@Article{CalegariAndrade_JPhysChemLett_2023_v14_p5560,
    author =   {Marcos F {Calegari Andrade} and Tuan Anh Pham},
    title =    {{Probing Confinement Effects on the Infrared Spectra of Water with Deep
             Potential Molecular Dynamics Simulations}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    24,
    pages =    {5560--5566},
    annote =   {The hydrogen-bond network of confined water is expected to deviate
             from that of the bulk liquid, yet probing these deviations remains a
             significant challenge. In this work, we combine large-scale molecular
             dynamics simulations with machine learning potential derived from
             first-principles calculations to examine the hydrogen bonding of water
             confined in carbon nanotubes (CNTs). We computed and compared the
             infrared spectrum (IR) of confined water to existing experiments to
             elucidate confinement effects. For CNTs with diameters >1.2 nm, we
             find that confinement imposes a monotonic effect on the hydrogen-bond
             network and on the IR spectrum of water. In contrast, confinement
             below 1.2 nm CNT diameter affects the water structure in a complex
             fashion, leading to a strong directional dependence of hydrogen
             bonding that varies nonlinearly with the CNT diameter. When integrated
             with existing IR measurements, our simulations provide a new
             interpretation for the IR spectrum of water confined in CNTs, pointing
             to previously unreported aspects of hydrogen bonding in this system.
             This work also offers a general platform for simulating water in CNTs
             with quantum accuracy on time and length scales beyond the reach of
             conventional first-principles approaches.},
    doi =      {10.1021/acs.jpclett.3c01054},
}


@Article{Wang_JPhysChemC_2023_v127_p11369,
    author =   {Jing Wang and Xin Wang and Hua Zhu and Dingguo Xu},
    title =    {{Investigating the Hydroxyl Reorientation in Hydroxyapatite Using
             Machine Learning Potentials}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    23,
    pages =    {11369--11377},
    doi =      {10.1021/acs.jpcc.3c02426},
}


@Article{Li_Unknown_2023_v133_pNone,
    author =   {Zhiqiang Li and Xinlei Duan and Linhua Liu and Jia-Yue Yang},
    title =    {{Temperature-dependent microwave dielectric permittivity of gallium
             oxide: A deep potential molecular dynamics study}},
    year =     2023,
    volume =   133,
    issue =    22,
    annote =   {<jats:p>The microwave (MW) dielectric permittivity of gallium oxide
             ({\ensuremath{\beta}}-Ga2O3) fundamentally determines its interaction
             with an electromagnetic wave in bulk power. Yet, there is a lack of
             experimental data due to limitations of high-temperature MW dielectric
             measurements and the large uncertainty under variable-temperature
             conditions. Herein, we develop a deep potential (DP) based on density
             functional theory (DFT) results and apply deep potential molecular
             dynamics (DPMD) for accurately predicting temperature-dependent MW
             dielectric permittivity of {\ensuremath{\beta}}-Ga2O3. The predicted
             energies and forces by DP demonstrate excellent agreement with DFT
             results, and DPMD successfully simulates systems up to 1280 atoms with
             quantum precision over nanosecond scales. Overall, the real part of
             the MW dielectric permittivity decreases with rising frequency, but
             the dielectric loss increases. The MW dielectric permittivity
             gradually increases as the temperature increases, which is closely
             related to the reduced dielectric relaxation time and increased static
             and high-frequency dielectric constants. Besides, the oxygen vacancy
             defects significantly reduce the relaxation time; however, augmenting
             the defect concentration will cause a slight rise in relaxation time.
             The electron localization function analysis reveals that more free
             electrons and low localization of electrons produced by high defect
             concentrations facilitate the increased relaxation time. This study
             provides an alternative route to investigate the temperature-dependent
             MW permittivity of {\ensuremath{\beta}}-Ga2O3, which attains prime
             importance for its potential applications in RF and power
             electronics.</jats:p>},
    doi =      {10.1063/5.0149447},
}


@Article{Zhuang_JPhysChemC_2023_v127_p10532,
    author =   {Yong-Bin Zhuang and Jun Cheng},
    title =    {{Deciphering the Anomalous Acidic Tendency of Terminal Water at
             Rutile(110){\textendash}Water Interfaces}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    22,
    pages =    {10532--10540},
    doi =      {10.1021/acs.jpcc.3c01870},
}


@Article{Caruso_JChemPhys_2023_v158_pNone,
    author =   {Cristina Caruso and Annalisa Cardellini and Martina Crippa and Daniele
             Rapetti and Giovanni M Pavan},
    title =    {{<i>Time</i>SOAP: Tracking high-dimensional fluctuations in complex
             molecular systems via time variations of SOAP spectra}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    21,
    annote =   {Many molecular systems and physical phenomena are controlled by local
             fluctuations and microscopic dynamical rearrangements of the
             constitutive interacting units that are often difficult to detect.
             This is the case, for example, of phase transitions, phase equilibria,
             nucleation events, and defect propagation, to mention a few. A
             detailed comprehension of local atomic environments and of their
             dynamic rearrangements is essential to understand such phenomena and
             also to draw structure-property relationships useful to unveil how to
             control complex molecular systems. Considerable progress in the
             development of advanced structural descriptors [e.g., Smooth Overlap
             of Atomic Position (SOAP), etc.] has certainly enhanced the
             representation of atomic-scale simulations data. However, despite such
             efforts, local dynamic environment rearrangements still remain
             difficult to elucidate. Here, exploiting the structurally rich
             description of atomic environments of SOAP and building on the concept
             of time-dependent local variations, we developed a SOAP-based
             descriptor, TimeSOAP ({\ensuremath{\tau}}SOAP), which essentially
             tracks time variations in local SOAP environments surrounding each
             molecule (i.e., each SOAP center) along ensemble trajectories. We
             demonstrate how analysis of the time-series {\ensuremath{\tau}}SOAP
             data and of their time derivatives allows us to detect dynamic domains
             and track instantaneous changes of local atomic arrangements (i.e.,
             local fluctuations) in a variety of molecular systems. The approach is
             simple and general, and we expect that it will help shed light on a
             variety of complex dynamical phenomena.},
    doi =      {10.1063/5.0147025},
}


@Article{Wang_Unknown_2023_v122_pNone,
    author =   {Q. Wang and C. H. Zheng and M. X. Li and L. Hu and H. P. Wang and B.
             Wei},
    title =    {{A genome dependence of metastable phase selection on atomic structure
             for undercooled liquid Nb90Si10 hypoeutectic alloy}},
    year =     2023,
    volume =   122,
    issue =    23,
    annote =   {<jats:p>The phase selection mechanism within undercooled liquid
             Nb90Si10 hypoeutectic alloy was investigated by electrostatic
             levitation technique combined with deep neural network molecular
             dynamics. A stepwise-solidification procedure was conducted, where the
             primary phase and eutectic microstructure successively solidified from
             undercooled liquid alloy and undercooled residual liquid,
             respectively. The intermetallic phase of the eutectic structure
             transfers from Nb3Si to {\ensuremath{\beta}}Nb5Si3 and finally into
             {\ensuremath{\alpha}}Nb5Si3 compound with the increase in liquid
             undercooling. The deep neural network molecular dynamic simulations
             have shown that the phase selection between Nb3Si and Nb5Si3 is mainly
             controlled by the short-range order of residual liquid, considering
             that the predominant short-range configuration transforms from Nb3Si-
             like to Nb5Si3-like structures. The {\ensuremath{\alpha}}Nb5Si3-like
             medium-range order, which is characterized by vertex-connected
             {\ensuremath{\langle}}0,2,8,4{\ensuremath{\rangle}} clusters, is shown
             to significantly influence the competitive nucleation of the
             {\ensuremath{\alpha}}Nb5Si3 and {\ensuremath{\beta}}Nb5Si3 phases. The
             residual liquid favors the {\ensuremath{\alpha}}Nb5Si3-like medium-
             range order rather than {\ensuremath{\beta}}Nb5Si3 at large
             undercoolings, which explains the transformation from
             {\ensuremath{\beta}}Nb5Si3 to {\ensuremath{\alpha}}Nb5Si3.</jats:p>},
    doi =      {10.1063/5.0152293},
}


@Article{Fronzi_Nanomaterials_2023_v13_p1832,
    author =   {Marco Fronzi and Roger D Amos and Rika Kobayashi},
    title =    {{Evaluation of Machine Learning Interatomic Potentials for Gold
             Nanoparticles{\textemdash}Transferability towards Bulk}},
    journal =  {Nanomaterials (Basel).},
    year =     2023,
    volume =   13,
    issue =    12,
    pages =    1832,
    annote =   {We analyse the efficacy of machine learning (ML) interatomic
             potentials (IP) in modelling gold (Au) nanoparticles. We have explored
             the transferability of these ML models to larger systems and
             established simulation times and size thresholds necessary for
             accurate interatomic potentials. To achieve this, we compared the
             energies and geometries of large Au nanoclusters using VASP and LAMMPS
             and gained better understanding of the number of VASP simulation
             timesteps required to generate ML-IPs that can reproduce the
             structural properties. We also investigated the minimum atomic size of
             the training set necessary to construct ML-IPs that accurately
             replicate the structural properties of large Au nanoclusters, using
             the LAMMPS-specific heat of the Au147 icosahedral as reference. Our
             findings suggest that minor adjustments to a potential developed for
             one system can render it suitable for other systems. These results
             provide further insight into the development of accurate interatomic
             potentials for modelling Au nanoparticles through machine learning
             techniques.},
    PMCID =    {PMC10303715},
    doi =      {10.3390/nano13121832},
}


@Article{Sun_PhysRevB_2023_v107_p224301,
    author =   {Huaijun Sun and Chao Zhang and Ling Tang and Renhai Wang and Weiyi Xia
             and Cai-Zhuang Wang},
    title =    {{Molecular dynamics simulation of Fe-Si alloys using a neural network
             machine learning potential}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   107,
    issue =    22,
    pages =    224301,
    doi =      {10.1103/PhysRevB.107.224301},
}


@Article{Qi_JMaterSci_2023_v58_p9515,
    author =   {Yongnian Qi and Xiaoguang Guo and Hao Wang and Shuohua Zhang and Ming
             Li and Ping Zhou and Dongming Guo},
    title =    {{Reversible densification and cooperative atomic movement induced
             {\textquotedblleft}compaction{\textquotedblright} in vitreous silica:
             a new sight from deep neural network interatomic potentials}},
    journal =  {J Mater Sci},
    year =     2023,
    volume =   58,
    issue =    23,
    pages =    {9515--9532},
    doi =      {10.1007/s10853-023-08599-w},
}


@Article{Wang_GeochimicaetCosmochimicaActa_2023_v350_p57,
    author =   {Kai Wang and Xiancai Lu and Xiandong Liu and Kun Yin},
    title =    {{Noble gas (He, Ne, and Ar) solubilities in high-pressure silicate
             melts calculated based on deep-potential modeling}},
    journal =  {Geochimica et Cosmochimica Acta},
    year =     2023,
    volume =   350,
    pages =    {57--68},
    doi =      {10.1016/j.gca.2023.03.032},
}


@Article{Zhao_IEEETransCircuitsSystI_2023_v70_p2439,
    author =   {Zhuoying Zhao and Ziling Tan and Pinghui Mo and Xiaonan Wang and Dan
             Zhao and Xin Zhang and Ming Tao and Jie Liu},
    title =    {{A Heterogeneous Parallel Non-von Neumann Architecture System for
             Accurate and Efficient Machine Learning Molecular Dynamics}},
    journal =  {IEEE Trans. Circuits Syst. I},
    year =     2023,
    volume =   70,
    issue =    6,
    pages =    {2439--2449},
    doi =      {10.1109/TCSI.2023.3255199},
}


@Article{Xie_SolarEnergyMaterialsandSolarCells_2023_v254_p112275,
    author =   {Yun Xie and Min Bu and Guiming Zou and Ye Zhang and Guimin Lu},
    title =    {{Molecular dynamics simulations of CaCl2{\textendash}NaCl molten salt
             based on the machine learning potentials}},
    journal =  {Solar Energy Materials and Solar Cells},
    year =     2023,
    volume =   254,
    pages =    112275,
    doi =      {10.1016/j.solmat.2023.112275},
}


@Article{Yeo_AppliedSurfaceScience_2023_v621_p156893,
    author =   {Kangmo Yeo and Sukmin Jeong},
    title =    {{Machine learning insight into h-BN growth on Pt(111) from atomic
             states}},
    journal =  {Applied Surface Science},
    year =     2023,
    volume =   621,
    pages =    156893,
    doi =      {10.1016/j.apsusc.2023.156893},
}


@Article{Achar_ACSApplMaterInterfaces_2023_v15_p25873,
    author =   {Siddarth K Achar and Leonardo Bernasconi and Ruby I DeMaio and Katlyn
             R Howard and J Karl Johnson},
    title =    {{In Silico Demonstration of Fast Anhydrous Proton Conduction on
             Graphanol}},
    journal =  {ACS Appl. Mater. Interfaces},
    year =     2023,
    volume =   15,
    issue =    21,
    pages =    {25873--25883},
    annote =   {Development of new materials capable of conducting protons in the
             absence of water is crucial for improving the performance, reducing
             the cost, and extending the operating conditions for proton exchange
             membrane fuel cells. We present detailed atomistic simulations showing
             that graphanol (hydroxylated graphane) will conduct protons
             anhydrously with very low diffusion barriers. We developed a deep
             learning potential (DP) for graphanol that has near-density functional
             theory accuracy but requires a very small fraction of the
             computational cost. We used our DP to calculate proton self-diffusion
             coefficients as a function of temperature, to estimate the overall
             barrier to proton diffusion, and to characterize the impact of thermal
             fluctuations as a function of system size. We propose and test a
             detailed mechanism for proton conduction on the surface of graphanol.
             We show that protons can rapidly hop along Grotthuss chains containing
             several hydroxyl groups aligned such that hydrogen bonds allow for
             conduction of protons forward and backward along the chain without
             hydroxyl group rotation. Long-range proton transport only takes place
             as new Grotthuss chains are formed by rotation of one or more hydroxyl
             groups in the chain. Thus, the overall diffusion barrier consists of a
             convolution of the intrinsic proton hopping barrier and the intrinsic
             hydroxyl rotation barrier. Our results provide a set of design rules
             for developing new anhydrous proton conducting membranes with even
             lower diffusion barriers.},
    PMCID =    {PMC10236431},
    doi =      {10.1021/acsami.3c04022},
}


@Article{Lu_JChemPhys_2023_v158_pNone,
    author =   {Jiajun Lu and Jinkai Wang and Kaiwei Wan and Ying Chen and Hao Wang
             and Xinghua Shi},
    title =    {{An accurate interatomic potential for the TiAlNb ternary alloy
             developed by deep neural network learning method}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    20,
    annote =   {The complex phase diagram and bonding nature of the TiAl system make
             it difficult to accurately describe its various properties and phases
             by traditional atomistic force fields. Here, we develop a machine
             learning interatomic potential with a deep neural network method for
             the TiAlNb ternary alloy based on a dataset built by first-principles
             calculations. The training set includes bulk elementary metals and
             intermetallic structures with slab and amorphous configurations. This
             potential is validated by comparing bulk properties-including lattice
             constant and elastic constants, surface energies, vacancy formation
             energies, and stacking fault energies-with their respective density
             functional theory values. Moreover, our potential could accurately
             predict the average formation energy and stacking fault energy of
             {\ensuremath{\gamma}}-TiAl doped with Nb. The tensile properties of
             {\ensuremath{\gamma}}-TiAl are simulated by our potential and verified
             by experiments. These results support the applicability of our
             potential under more practical conditions.},
    doi =      {10.1063/5.0147720},
}


@Article{Li_JPhysChemC_2023_v127_p9750,
    author =   {Lesheng Li and Marcos F. {Calegari Andrade} and Roberto Car and
             Annabella Selloni and Emily A. Carter},
    title =    {{Characterizing Structure-Dependent TiS<sub>2</sub>/Water Interfaces
             Using Deep-Neural-Network-Assisted Molecular Dynamics}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    20,
    pages =    {9750--9758},
    doi =      {10.1021/acs.jpcc.2c08581},
}


@Article{Mathur_JPhysChemB_2023_v127_p4562,
    author =   {Reha Mathur and Maria Carolina Muniz and Shuwen Yue and Roberto Car
             and Athanassios Z Panagiotopoulos},
    title =    {{First-Principles-Based Machine Learning Models for Phase Behavior and
             Transport Properties of CO<sub>2</sub>}},
    journal =  {J. Phys. Chem. B},
    year =     2023,
    volume =   127,
    issue =    20,
    pages =    {4562--4569},
    annote =   {In this work, we construct distinct first-principles-based machine-
             learning models of CO2, reproducing the potential energy surface of
             the PBE-D3, BLYP-D3, SCAN, and SCAN-rvv10 approximations of density
             functional theory. We employ the Deep Potential methodology to develop
             the models and consequently achieve a significant computational
             efficiency over ab initio molecular dynamics (AIMD) that allows for
             larger system sizes and time scales to be explored. Although our
             models are trained only with liquid-phase configurations, they are
             able to simulate a stable interfacial system and predict vapor-liquid
             equilibrium properties, in good agreement with results from the
             literature. Because of the computational efficiency of the models, we
             are also able to obtain transport properties, such as viscosity and
             diffusion coefficients. We find that the SCAN-based model presents a
             temperature shift in the position of the critical point, while the
             SCAN-rvv10-based model shows improvement but still exhibits a
             temperature shift that remains approximately constant for all
             properties investigated in this work. We find that the BLYP-D3-based
             model generally performs better for the liquid phase and vapor-liquid
             equilibrium properties, but the PBE-D3-based model is better suited
             for predicting transport properties.},
    doi =      {10.1021/acs.jpcb.3c00610},
}


@Article{Sanchez-Burgos_JChemPhys_2023_v158_pNone,
    author =   {Ignacio Sanchez-Burgos and Maria Carolina Muniz and Jorge R Espinosa
             and Athanassios Z Panagiotopoulos},
    title =    {{A Deep Potential model for liquid{\textendash}vapor equilibrium and
             cavitation rates of water}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    18,
    annote =   {Computational studies of liquid water and its phase transition into
             vapor have traditionally been performed using classical water models.
             Here, we utilize the Deep Potential methodology-a machine learning
             approach-to study this ubiquitous phase transition, starting from the
             phase diagram in the liquid-vapor coexistence regime. The machine
             learning model is trained on ab{~}initio energies and forces based on
             the SCAN density functional, which has been previously shown to
             reproduce solid phases and other properties of water. Here, we compute
             the surface tension, saturation pressure, and enthalpy of vaporization
             for a range of temperatures spanning from 300 to 600{~}K and evaluate
             the Deep Potential model performance against experimental results and
             the semiempirical TIP4P/2005 classical model. Moreover, by employing
             the seeding technique, we evaluate the free energy barrier and
             nucleation rate at negative pressures for the isotherm of 296.4{~}K.
             We find that the nucleation rates obtained from the Deep Potential
             model deviate from those computed for the TIP4P/2005 water model due
             to an underestimation in the surface tension from the Deep Potential
             model. From analysis of the seeding simulations, we also evaluate the
             Tolman length for the Deep Potential water model, which is (0.091
             {\ensuremath{\pm}} 0.008) nm at 296.4{~}K. Finally, we identify that
             water molecules display a preferential orientation in the liquid-vapor
             interface, in which H atoms tend to point toward the vapor phase to
             maximize the enthalpic gain of interfacial molecules. We find that
             this behavior is more pronounced for planar interfaces than for the
             curved interfaces in bubbles. This work represents the first
             application of Deep Potential models to the study of liquid-vapor
             coexistence and water cavitation.},
    doi =      {10.1063/5.0144500},
}


@Article{Giese_JChemPhys_2023_v158_pNone,
    author =   {Timothy J Giese and Darrin M York},
    title =    {{Estimation of frequency factors for the calculation of kinetic isotope
             effects from classical and path integral free energy simulations}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    17,
    annote =   {We use the modified Bigeleisen-Mayer equation to compute kinetic
             isotope effect values for non-enzymatic phosphoryl transfer reactions
             from classical and path integral molecular dynamics umbrella sampling.
             The modified form of the Bigeleisen-Mayer equation consists of a ratio
             of imaginary mode vibrational frequencies and a contribution arising
             from the isotopic substitution's effect on the activation free energy,
             which can be computed from path integral simulation. In the present
             study, we describe a practical method for estimating the frequency
             ratio correction directly from umbrella sampling in a manner that does
             not require normal mode analysis of many geometry optimized
             structures. Instead, the method relates the frequency ratio to the
             change in the mass weighted coordinate representation of the minimum
             free energy path at the transition state induced by isotopic
             substitution. The method is applied to the calculation of 16/18O and
             32/34S primary kinetic isotope effect values for six non-enzymatic
             phosphoryl transfer reactions. We demonstrate that the results are
             consistent with the analysis of geometry optimized transition state
             ensembles using the traditional Bigeleisen-Mayer equation. The method
             thus presents a new practical tool to enable facile calculation of
             kinetic isotope effect values for complex chemical reactions in the
             condensed phase.},
    PMCID =    {PMC10154067},
    doi =      {10.1063/5.0147218},
}


@Article{Chen_PhysRevMaterials_2023_v7_p053603,
    author =   {Tao Chen and Fengbo Yuan and Jianchuan Liu and Huayun Geng and Linfeng
             Zhang and Han Wang and Mohan Chen},
    title =    {{Modeling the high-pressure solid and liquid phases of tin from deep
             potentials with  <i>ab initio</i>  accuracy}},
    journal =  {Phys. Rev. Materials},
    year =     2023,
    volume =   7,
    issue =    5,
    pages =    053603,
    doi =      {10.1103/PhysRevMaterials.7.053603},
}


@Article{Han_Nanomaterials_2023_v13_p1576,
    author =   {Jinsen Han and Qiyu Zeng and Ke Chen and Xiaoxiang Yu and Jiayu Dai},
    title =    {{Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine
             Learning Potential}},
    journal =  {Nanomaterials (Basel).},
    year =     2023,
    volume =   13,
    issue =    9,
    pages =    1576,
    annote =   {The two-dimensional post-transition-metal chalcogenides, particularly
             indium selenide (InSe), exhibit salient carrier transport properties
             and evince extensive interest for broad applications. A comprehensive
             understanding of thermal transport is indispensable for thermal
             management. However, theoretical predictions on thermal transport in
             the InSe system are found in disagreement with experimental
             measurements. In this work, we utilize both the Green-Kubo approach
             with deep potential (GK-DP), together with the phonon Boltzmann
             transport equation with density functional theory (BTE-DFT) to
             investigate the thermal conductivity ({\ensuremath{\kappa}}) of InSe
             monolayer. The {\ensuremath{\kappa}} calculated by GK-DP is 9.52 W/mK
             at 300 K, which is in good agreement with the experimental value,
             while the {\ensuremath{\kappa}} predicted by BTE-DFT is 13.08 W/mK.
             After analyzing the scattering phase space and cumulative
             {\ensuremath{\kappa}} by mode-decomposed method, we found that, due to
             the large energy gap between lower and upper optical branches, the
             exclusion of four-phonon scattering in BTE-DFT underestimates the
             scattering phase space of lower optical branches due to large group
             velocities, and thus would overestimate their contribution to
             {\ensuremath{\kappa}}. The temperature dependence of
             {\ensuremath{\kappa}} calculated by GK-DP also demonstrates the effect
             of higher-order phonon scattering, especially at high temperatures.
             Our results emphasize the significant role of four-phonon scattering
             in InSe monolayer, suggesting that combining molecular dynamics with
             machine learning potential is an accurate and efficient approach to
             predict thermal transport.},
    PMCID =    {PMC10180940},
    doi =      {10.3390/nano13091576},
}


@Article{Yuan_EarthandPlanetaryScienceLetters_2023_v609_p118084,
    author =   {Liang Yuan and Gerd Steinle-Neumann},
    title =    {{Hydrogen distribution between the Earth's inner and outer core}},
    journal =  {Earth and Planetary Science Letters},
    year =     2023,
    volume =   609,
    pages =    118084,
    doi =      {10.1016/j.epsl.2023.118084},
}


@Article{He_ComputationalMaterialsScience_2023_v223_p112111,
    author =   {Xi He and Jinde Liu and Chen Yang and Gang Jiang},
    title =    {{Predicting thermodynamic stability of magnesium alloys in machine
             learning}},
    journal =  {Computational Materials Science},
    year =     2023,
    volume =   223,
    pages =    112111,
    doi =      {10.1016/j.commatsci.2023.112111},
}


@Article{Hu_JPhysChemLett_2023_v14_p3677,
    author =   {Taiping Hu and Fu-Zhi Dai and Guobing Zhou and Xiaoxu Wang and
             Shenzhen Xu},
    title =    {{Unraveling the Dynamic Correlations between Transition Metal Migration
             and the Oxygen Dimer Formation in the Highly Delithiated
             Li<sub><i>x</i></sub>CoO<sub>2</sub> Cathode}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    15,
    pages =    {3677--3684},
    annote =   {The voltage-window expansion can increase the practical capacity of
             LixCoO2 cathodes, but it would lead to serious structural degradations
             and oxygen release induced by transition metal (TM) migration.
             Therefore, it is crucial to understand the dynamic correlations
             between the TM migration and the oxygen dimer formation. Here,
             machine-learning-potential-assisted molecular dynamics simulations
             combined with enhanced sampling techniques are performed to resolve
             the above question using a representative CoO2 model. Our results show
             that the occurrence of the Co migration exhibits local
             characteristics. The formation of the Co vacancy cluster is necessary
             for the oxygen dimer generation. The introduction of the Ti dopant can
             significantly increase the kinetic barrier of the Co ion migration and
             thus effectively suppress the formation of the Co vacancy cluster. Our
             work reveals atomic-scale dynamic correlations between the TM
             migration and the oxygen sublattice's instability and provides
             insights about the dopant's promotion of the structural stability.},
    doi =      {10.1021/acs.jpclett.3c00506},
}


@Article{Luo_JPhysChemC_2023_v127_p7071,
    author =   {Kun Luo and Yidi Shen and Jun Li and Qi An},
    title =    {{Pressure-Induced Stability of Methane Hydrate from Machine Learning
             Force Field Simulations}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    15,
    pages =    {7071--7077},
    doi =      {10.1021/acs.jpcc.2c09121},
}


@Article{Ghosh_JPhysCondensMatter_2023_v35_p154002,
    author =   {Maitrayee Ghosh and Shuai Zhang and Lianming Hu and S X Hu},
    title =    {{Cooperative diffusion in body-centered cubic iron in Earth and super-
             Earths{\textquoteright} inner core conditions}},
    journal =  {J. Phys. Condens. Matter},
    year =     2023,
    volume =   35,
    issue =    15,
    pages =    154002,
    annote =   {The physical chemistry of iron at the inner-core conditions is key to
             understanding the evolution and habitability of Earth and super-Earth
             planets. Based on full first-principles simulations, we report
             cooperative diffusion along the longitudinally
             fast{\ensuremath{\langle}}111{\ensuremath{\rangle}}directions of body-
             centered cubic (bcc) iron in temperature ranges of up to 2000-4000 K
             below melting and pressures of {\ensuremath{\sim}}300-4000{\,}GPa. The
             diffusion is due to the low energy barrier in the corresponding
             direction and is accompanied by mechanical and dynamical stability, as
             well as strong elastic anisotropy of bcc iron. These findings provide
             a possible explanation for seismological signatures of the Earth's
             inner core, particularly the positive correlation between P wave
             velocity and attenuation. The diffusion can also change the detailed
             mechanism of core convection by increasing the diffusivity and
             electrical conductivity and lowering the viscosity. The results need
             to be considered in future geophysical and planetary models and should
             motivate future studies of materials under extreme conditions.},
    doi =      {10.1088/1361-648X/acba71},
}


@Article{Zhao_JPhysChemC_2023_v127_p6852,
    author =   {C. Y. Zhao and Y. B. Tao and Y. He},
    title =    {{Microstructure and Thermophysical Property Prediction for Chloride
             Composite Phase Change Materials: A Deep Potential Molecular Dynamics
             Study}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    14,
    pages =    {6852--6860},
    doi =      {10.1021/acs.jpcc.2c08589},
}


@Article{Chang_PhysChemChemPhys_2023_v25_p12841,
    author =   {Xiaoya Chang and Qingzhao Chu and Dongping Chen},
    title =    {{Monitoring the melting behavior of boron nanoparticles using a neural
             network potential}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2023,
    volume =   25,
    issue =    18,
    pages =    {12841--12853},
    annote =   {The melting behavior of metal additives is fundamental for various
             propulsion and energy-conversion applications. A neural network
             potential (NNP) is proposed to examine the size-dependent melting
             behaviors of boron nanoparticles. Our NNP model is proven to possess a
             desirable computational efficiency and retain ab initio accuracy,
             allowing investigation of the physicochemical properties of bulk boron
             crystals from an atomic perspective. In this work, a series of NNP-
             based molecular dynamics simulations were conducted and numerical
             evidence of the size-dependent melting behavior of boron nanoparticles
             with diameters from 3 to 6 nm was reported for the first time.
             Evolution of the intermolecular energy and the Lindemann index are
             used to monitor the melting process. A liquid layer forms on the
             particle surface and further expands with increased temperature. Once
             the liquid layer reaches the core region, the particle is completely
             molten. The reduced melting temperature of the boron nanoparticle
             decreases with its particle size following a linear relationship with
             reciprocal size, similar to other commonly used metals (Al and Mg).
             Additionally, boron nanoparticles are more sensitive to particle size
             than Al particles and less sensitive than Mg particles. These findings
             provide an atomistic perspective for developing manufacturing
             techniques and tailoring combustion performance in practical
             applications.},
    doi =      {10.1039/d3cp00571b},
}


@Article{Wu_JPhysChemC_2023_v127_p6262,
    author =   {Jiawei Wu and Dingming Chen and Jianfu Chen and Haifeng Wang},
    title =    {{Structural and Composition Evolution of Palladium Catalyst for CO
             Oxidation under Steady-State Reaction Conditions}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    13,
    pages =    {6262--6270},
    doi =      {10.1021/acs.jpcc.2c07877},
}


@Article{JaffrelotInizan_ChemSci_2023_v14_p5438,
    author =   {Th{\'e}o {Jaffrelot Inizan} and Thomas Pl{\'e} and Olivier Adjoua and
             Pengyu Ren and Hatice G{\"o}kcan and Olexandr Isayev and Louis
             Lagard{\`e}re and Jean-Philip Piquemal},
    title =    {{Scalable hybrid deep neural networks/polarizable potentials
             biomolecular simulations including long-range effects}},
    journal =  {Chem. Sci.},
    year =     2023,
    volume =   14,
    issue =    20,
    pages =    {5438--5452},
    annote =   {Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular
             dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep
             Neural Network (DNN) models. Deep-HP increases DNNs' MD capabilities
             by orders of magnitude offering access to ns simulations for 100k-atom
             biosystems while offering the possibility of coupling DNNs to any
             classical (FFs) and many-body polarizable (PFFs) force fields. It
             allows therefore the introduction of the ANI-2X/AMOEBA hybrid
             polarizable potential designed for ligand binding studies where
             solvent-solvent and solvent-solute interactions are computed with the
             AMOEBA PFF while solute-solute ones are computed by the ANI-2X DNN.
             ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range
             interactions via an efficient Particle Mesh Ewald implementation while
             preserving ANI-2X's solute short-range quantum mechanical accuracy.
             The DNN/PFF partition can be user-defined allowing for hybrid
             simulations to include key ingredients of biosimulation such as
             polarizable solvents, polarizable counter ions, etc.{\textellipsis}
             ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy
             focusing on the model's contributions to low-frequency modes of
             nuclear forces. It primarily evaluates AMOEBA forces while including
             ANI-2X ones only via correction-steps resulting in an order of
             magnitude acceleration over standard Velocity Verlet integration.
             Simulating more than 10 {\ensuremath{\mu}}s, we compute
             charged/uncharged ligand solvation free energies in 4 solvents, and
             absolute binding free energies of host-guest complexes from SAMPL
             challenges. ANI-2X/AMOEBA average errors are discussed in terms of
             statistical uncertainty and appear in the range of chemical accuracy
             compared to experiment. The availability of the Deep-HP computational
             platform opens the path towards large-scale hybrid DNN simulations, at
             force-field cost, in biophysics and drug discovery.},
    PMCID =    {PMC10208042},
    doi =      {10.1039/d2sc04815a},
}


@Article{Liu_ACSMaterialsLett_2023_v5_p1009,
    author =   {Jiahui Liu and Shuo Wang and Yoshiyuki Kawazoe and Qiang Sun},
    title =    {{A New Spinel Chloride Solid Electrolyte with High Ionic Conductivity
             and Stability for Na-Ion Batteries}},
    journal =  {ACS Materials Lett.},
    year =     2023,
    volume =   5,
    issue =    4,
    pages =    {1009--1017},
    doi =      {10.1021/acsmaterialslett.3c00119},
}


@Article{Xu_Nanomaterials_2023_v13_p1352,
    author =   {Hui Xu and Zeyuan Li and Zhaofu Zhang and Sheng Liu and Shengnan Shen
             and Yuzheng Guo},
    title =    {{High-Accuracy Neural Network Interatomic Potential for Silicon Nitride}},
    journal =  {Nanomaterials (Basel).},
    year =     2023,
    volume =   13,
    issue =    8,
    pages =    1352,
    annote =   {In the field of machine learning (ML) and data science, it is
             meaningful to use the advantages of ML to create reliable interatomic
             potentials. Deep potential molecular dynamics (DEEPMD) are one of the
             most useful methods to create interatomic potentials. Among ceramic
             materials, amorphous silicon nitride (SiNx) features good electrical
             insulation, abrasion resistance, and mechanical strength, which is
             widely applied in industries. In our work, a neural network potential
             (NNP) for SiNx was created based on DEEPMD, and the NNP is confirmed
             to be applicable to the SiNx model. The tensile tests were simulated
             to compare the mechanical properties of SiNx with different
             compositions based on the molecular dynamic method coupled with NNP.
             Among these SiNx, Si3N4 has the largest elastic modulus (E) and yield
             stress ({\ensuremath{\sigma}}s), showing the desired mechanical
             strength owing to the largest coordination numbers (CN) and radial
             distribution function (RDF). The RDFs and CNs decrease with the
             increase of x; meanwhile, E and {\ensuremath{\sigma}}s of SiNx
             decrease when the proportion of Si increases. It can be concluded that
             the ratio of nitrogen to silicon can reflect the RDFs and CNs in micro
             level and macro mechanical properties of SiNx to a large extent.},
    PMCID =    {PMC10145480},
    doi =      {10.3390/nano13081352},
}


@Article{Wang_Unknown_2023_v12_p803,
    author =   {Yinan Wang and Bo Wen and Xingjian Jiao and Ya Li and Lei Chen and
             Yujin Wang and Fu-Zhi Dai},
    title =    {{The highest melting point material: Searched by Bayesian global
             optimization with deep potential molecular dynamics}},
    year =     2023,
    volume =   12,
    issue =    4,
    pages =    {803--814},
    doi =      {10.26599/JAC.2023.9220721},
}


@Article{Li_InternationalJournalofPlasticity_2023_v163_p103552,
    author =   {Jun Li and Kun Luo and Qi An},
    title =    {{Atomic structure, stability, and dissociation of dislocations in
             cadmium telluride}},
    journal =  {International Journal of Plasticity},
    year =     2023,
    volume =   163,
    pages =    103552,
    doi =      {10.1016/j.ijplas.2023.103552},
}


@Article{Bu_JournalofMolecularLiquids_2023_v375_p120689,
    author =   {Min Bu and Taixi Feng and Guimin Lu},
    title =    {{Prediction on local structure and properties of LiCl-KCl-AlCl3 ternary
             molten salt with deep learning potential}},
    journal =  {Journal of Molecular Liquids},
    year =     2023,
    volume =   375,
    pages =    120689,
    doi =      {10.1016/j.molliq.2022.120689},
}


@Article{Cioni_JChemPhys_2023_v158_p124701,
    author =   {Matteo Cioni and Daniela Polino and Daniele Rapetti and Luca Pesce and
             Massimo {Delle Piane} and Giovanni M Pavan},
    title =    {{Innate dynamics and identity crisis of a metal surface unveiled by
             machine learning of atomic environments}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    12,
    pages =    124701,
    annote =   {Metals are traditionally considered hard matter. However, it is well
             known that their atomic lattices may become dynamic and undergo
             reconfigurations even well below the melting temperature. The innate
             atomic dynamics of metals is directly related to their bulk and
             surface properties. Understanding their complex structural dynamics
             is, thus, important for many applications but is not easy. Here, we
             report deep-potential molecular dynamics simulations allowing to
             resolve at an atomic resolution the complex dynamics of various types
             of copper (Cu) surfaces, used as an example, near the H{\"u}ttig
             ({\ensuremath{\sim}}1/3 of melting) temperature. The development of
             deep neural network potential trained on density functional theory
             calculations provides a dynamically accurate force field that we use
             to simulate large atomistic models of different Cu surface types. A
             combination of high-dimensional structural descriptors and
             unsupervized machine learning allows identifying and tracking all the
             atomic environments (AEs) emerging in the surfaces at finite
             temperatures. We can directly observe how AEs that are non-native in a
             specific (ideal) surface, but that are, instead, typical of other
             surface types, continuously emerge/disappear in that surface in
             relevant regimes in dynamic equilibrium with the native ones. Our
             analyses allow estimating the lifetime of all the AEs populating these
             Cu surfaces and to reconstruct their dynamic interconversions
             networks. This reveals the elusive identity of these metal surfaces,
             which preserve their identity only in part and in part transform into
             something else under relevant conditions. This also proposes a concept
             of "statistical identity" for metal surfaces, which is key to
             understanding their behaviors and properties.},
    doi =      {10.1063/5.0139010},
}


@Article{Zeng_JChemPhys_2023_v158_p124110,
    author =   {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York},
    title =    {{Modern semiempirical electronic structure methods and machine learning
             potentials for drug discovery: Conformers, tautomers, and protonation
             states}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    12,
    pages =    124110,
    annote =   {Modern semiempirical electronic structure methods have considerable
             promise in drug discovery as universal "force fields" that can
             reliably model biological and drug-like molecules, including
             alternative tautomers and protonation states. Herein, we compare the
             performance of several neglect of diatomic differential overlap-based
             semiempirical (MNDO/d, AM1, PM6, PM6-D3H4X, PM7, and ODM2), density-
             functional tight-binding based (DFTB3, DFTB/ChIMES, GFN1-xTB, and
             GFN2-xTB) models with pure machine learning potentials (ANI-1x and
             ANI-2x) and hybrid quantum mechanical/machine learning potentials
             (AIQM1 and QD{\ensuremath{\pi}}) for a wide range of data computed at
             a consistent {\ensuremath{\omega}}B97X/6-31G* level of theory (as in
             the ANI-1x database). This data includes conformational energies,
             intermolecular interactions, tautomers, and protonation states.
             Additional comparisons are made to a set of natural and synthetic
             nucleic acids from the artificially expanded genetic information
             system that has important implications for the design of new
             biotechnology and therapeutics. Finally, we examine the acid/base
             chemistry relevant for RNA cleavage reactions catalyzed by small
             nucleolytic ribozymes, DNAzymes, and ribonucleases. Overall, the
             hybrid quantum mechanical/machine learning potentials appear to be the
             most robust for these datasets, and the recently developed
             QD{\ensuremath{\pi}} model performs exceptionally well, having
             especially high accuracy for tautomers and protonation states relevant
             to drug discovery.},
    PMCID =    {PMC10052497},
    doi =      {10.1063/5.0139281},
}


@Article{Zheng_ACSNano_2023_v17_p5579,
    author =   {Bowen Zheng and Felipe Lopes Oliveira and Rodrigo {Neumann Barros
             Ferreira} and Mathias Steiner and Hendrik Hamann and Grace X Gu and
             Binquan Luan},
    title =    {{Quantum Informed Machine-Learning Potentials for Molecular Dynamics
             Simulations of CO<sub>2</sub>{\textquoteright}s Chemisorption and
             Diffusion in Mg-MOF-74}},
    journal =  {ACS Nano},
    year =     2023,
    volume =   17,
    issue =    6,
    pages =    {5579--5587},
    annote =   {Among various porous solids for gas separation and purification,
             metal-organic frameworks (MOFs) are promising materials that
             potentially combine high CO2 uptake and CO2/N2 selectivity. So far,
             within the hundreds of thousands of MOF structures known today, it
             remains a challenge to computationally identify the best suited
             species. First principle-based simulations of CO2 adsorption in MOFs
             would provide the necessary accuracy; however, they are impractical
             due to the high computational cost. Classical force field-based
             simulations would be computationally feasible; however, they do not
             provide sufficient accuracy. Thus, the entropy contribution that
             requires both accurate force fields and sufficiently long computing
             time for sampling is difficult to obtain in simulations. Here, we
             report quantum-informed machine-learning force fields (QMLFFs) for
             atomistic simulations of CO2 in MOFs. We demonstrate that the method
             has a much higher computational efficiency
             ({\ensuremath{\sim}}1000{\texttimes}) than the first-principle one
             while maintaining the quantum-level accuracy. As a proof of concept,
             we show that the QMLFF-based molecular dynamics simulations of CO2 in
             Mg-MOF-74 can predict the binding free energy landscape and the
             diffusion coefficient close to experimental values. The combination of
             machine learning and atomistic simulation helps achieve more accurate
             and efficient in silico evaluations of the chemisorption and diffusion
             of gas molecules in MOFs.},
    doi =      {10.1021/acsnano.2c11102},
}


@Article{Li_InternationalJournalofMechanicalSciences_2023_v242_p107998,
    author =   {Jun Li and Qi An},
    title =    {{Nanotwinning-induced pseudoplastic deformation in boron carbide under
             low temperature}},
    journal =  {International Journal of Mechanical Sciences},
    year =     2023,
    volume =   242,
    pages =    107998,
    doi =      {10.1016/j.ijmecsci.2022.107998},
}


@Article{Wu_JPhysChemLett_2023_v14_p2208,
    author =   {Zhihong Wu and Wen-Jin Yin and Bo Wen and Dongwei Ma and Li-Min Liu},
    title =    {{Oxygen Vacancy Diffusion in Rutile TiO<sub>2</sub>: Insight from Deep
             Neural Network Potential Simulations}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    8,
    pages =    {2208--2214},
    annote =   {Defects play a crucial role in the surface reactivity and electronic
             engineering of titanium dioxide (TiO2). In this work, we have used an
             active learning method to train deep neural network potentials from
             the ab initio data of a defective TiO2 surface. Validations show a
             good consistency between the deep potentials (DPs) and density
             functional theory (DFT) results. Therefore, the DPs were further
             applied on the extended surface and executed for nanoseconds. The
             results show that the oxygen vacancy at various sites are very stable
             under 330 K. However, some unstable defect sites will convert to the
             most favorable ones after tens or hundreds of picoseconds, while the
             temperature was elevated to 500 K. The DP predicated barriers of
             oxygen vacancy diffusion were similar to those of DFT. These results
             show that machine-learning trained DPs could accelerate the molecular
             dynamics with a DFT-level accuracy and promote people's understanding
             of the microscopic mechanism of fundamental reactions.},
    doi =      {10.1021/acs.jpclett.2c03827},
}


@Article{Balyakin_JetpLett_2023_v117_p370,
    author =   {I. A. Balyakin and R. E. Ryltsev and N. M. Chtchelkatchev},
    title =    {{Liquid{\textendash}Crystal Structure Inheritance in Machine Learning
             Potentials for Network-Forming Systems}},
    journal =  {Jetp Lett.},
    year =     2023,
    volume =   117,
    issue =    5,
    pages =    {370--376},
    annote =   {<jats:p>It has been studied whether machine learning interatomic
             potentials parameterized with only disordered configurations
             corresponding to liquid can describe the properties of crystalline
             phases and predict their structure. The study has been performed for a
             network-forming system SiO<jats:sub>2</jats:sub>, which has numerous
             polymorphic phases significantly different in structure and density.
             Using only high-temperature disordered configurations, a machine
             learning interatomic potential based on artificial neural networks
             (DeePMD model) has been parameterized. The potential reproduces well
             ab initio dependences of the energy on the volume and the vibrational
             density of states for all considered tetra- and octahedral crystalline
             phases of SiO<jats:sub>2</jats:sub>. Furthermore, the combination of
             the evolutionary algorithm and the developed DeePMD potential has made
             it possible to reproduce the really observed crystalline structures of
             SiO<jats:sub>2</jats:sub>. Such a good liquid{\textendash}crystal
             portability of the machine learning interatomic potential opens
             prospects for the simulation of the structure and properties of new
             systems for which experimental information on crystalline phases is
             absent.</jats:p>},
    doi =      {10.1134/S0021364023600234},
}


@Article{Wang_PhysRevMaterials_2023_v7_p034601,
    author =   {Zhi-Hao Wang and Xuan-Yan Chen and Zhen Zhang and Xie Zhang and Su-
             Huai Wei},
    title =    {{Profiling the off-center atomic displacements in CuCl at finite
             temperatures with a deep-learning potential}},
    journal =  {Phys. Rev. Materials},
    year =     2023,
    volume =   7,
    issue =    3,
    pages =    034601,
    doi =      {10.1103/PhysRevMaterials.7.034601},
}


@Article{Li_CementandConcreteResearch_2023_v165_p107092,
    author =   {Yunjian Li and Hui Pan and Zongjin Li},
    title =    {{Unravelling the dissolution dynamics of silicate minerals by deep
             learning molecular dynamics simulation: A case of dicalcium silicate}},
    journal =  {Cement and Concrete Research},
    year =     2023,
    volume =   165,
    pages =    107092,
    doi =      {10.1016/j.cemconres.2023.107092},
}


@Article{Sterkhova_JournalofPhysicsandChemistryofSolids_2023_v174_p111143,
    author =   {I.V. Sterkhova and L.V. Kamaeva and V.I. Lad'yanov and N.M.
             Chtchelkatchev},
    title =    {{Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax
             (x=0{\textendash}2) melts: Experiment and machine learning}},
    journal =  {Journal of Physics and Chemistry of Solids},
    year =     2023,
    volume =   174,
    pages =    111143,
    doi =      {10.1016/j.jpcs.2022.111143},
}


@Article{Zhai_JChemPhys_2023_v158_p084111,
    author =   {Yaoguang Zhai and Alessandro Caruso and Sigbj{\o}rn L{\o}land Bore and
             Zhishang Luo and Francesco Paesani},
    title =    {{A {\textquotedblleft}short blanket{\textquotedblright} dilemma for a
             state-of-the-art neural network potential for water: Reproducing
             experimental properties or the physics of the underlying many-body
             interactions?}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    8,
    pages =    084111,
    annote =   {Deep neural network (DNN) potentials have recently gained popularity
             in computer simulations of a wide range of molecular systems, from
             liquids to materials. In this study, we explore the possibility of
             combining the computational efficiency of the DeePMD framework and the
             demonstrated accuracy of the MB-pol data-driven, many-body potential
             to train a DNN potential for large-scale simulations of water across
             its phase diagram. We find that the DNN potential is able to reliably
             reproduce the MB-pol results for liquid water, but provides a less
             accurate description of the vapor-liquid equilibrium properties. This
             shortcoming is traced back to the inability of the DNN potential to
             correctly represent many-body interactions. An attempt to explicitly
             include information about many-body effects results in a new DNN
             potential that exhibits the opposite performance, being able to
             correctly reproduce the MB-pol vapor-liquid equilibrium properties,
             but losing accuracy in the description of the liquid properties. These
             results suggest that DeePMD-based DNN potentials are not able to
             correctly "learn" and, consequently, represent many-body interactions,
             which implies that DNN potentials may have limited ability to predict
             the properties for state points that are not explicitly included in
             the training process. The computational efficiency of the DeePMD
             framework can still be exploited to train DNN potentials on data-
             driven many-body potentials, which can thus enable large-scale,
             "chemically accurate" simulations of various molecular systems, with
             the caveat that the target state points must have been adequately
             sampled by the reference data-driven many-body potential in order to
             guarantee a faithful representation of the associated properties.},
    doi =      {10.1063/5.0142843},
}


@Article{Xiao_Unknown_2023_v133_pNone,
    author =   {R. L. Xiao and Q. Wang and J. Y. Qin and J. F. Zhao and Y. Ruan and H.
             P. Wang and H. Li and B. Wei},
    title =    {{A deep learning approach to predict thermophysical properties of
             metastable liquid Ti-Ni-Cr-Al alloy}},
    year =     2023,
    volume =   133,
    issue =    8,
    annote =   {<jats:p>The physical properties of liquid alloy are crucial for many
             science fields. However, acquiring these properties remains
             challenging. By means of the deep neural network (DNN), here we
             presented a deep learning interatomic potential for the
             Ti{\textendash}Ni{\textendash}Cr{\textendash}Al liquid system.
             Meanwhile, the thermophysical properties of the
             Ti{\textendash}Ni{\textendash}Cr{\textendash}Al liquid alloy were
             experimentally measured by electrostatic levitation and
             electromagnetic levitation technologies. The DNN potential predicted
             this liquid system accurately in terms of both atomic structures and
             thermophysical properties, and the results were in agreement with the
             ab initio molecular dynamics calculation and the experimental values.
             A further study on local structure carried out by Voronoi polyhedron
             analysis showed that the cluster exhibited a tendency to transform
             into high-coordinated cluster with a decrease in the temperature,
             indicating the enhancement of local structure stability. This
             eventually contributed to the linear increase in the density and
             surface tension, and the exponential variation in the viscosity and
             the diffusion coefficient with the rise of undercooling.</jats:p>},
    doi =      {10.1063/5.0138001},
}


@Article{Zeng_JChemTheoryComput_2023_v19_p1261,
    author =   {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York},
    title =    {{QD{\ensuremath{\pi}}: A Quantum Deep Potential Interaction Model for
             Drug Discovery}},
    journal =  {J. Chem. Theory Comput.},
    year =     2023,
    volume =   19,
    issue =    4,
    pages =    {1261--1275},
    annote =   {We report QD{\ensuremath{\pi}}-v1.0 for modeling the internal energy
             of drug molecules containing H, C, N, and O atoms. The
             QD{\ensuremath{\pi}} model is in the form of a quantum
             mechanical/machine learning potential correction
             (QM/{\ensuremath{\Delta}}-MLP) that uses a fast third-order self-
             consistent density-functional tight-binding (DFTB3/3OB) model that is
             corrected to a quantitatively high-level of accuracy through a deep-
             learning potential (DeepPot-SE). The model has the advantage that it
             is able to properly treat electrostatic interactions and handle
             changes in charge/protonation states. The model is trained against
             reference data computed at the {\ensuremath{\omega}}B97X/6-31G* level
             (as in the ANI-1x data set) and compared to several other approximate
             semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3,
             MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QD{\ensuremath{\pi}}
             model is demonstrated to be accurate for a wide range of intra- and
             intermolecular interactions (despite its intended use as an internal
             energy model) and has shown to perform exceptionally well for relative
             protonation/deprotonation energies and tautomers. An example
             application to model reactions involved in RNA strand cleavage
             catalyzed by protein and nucleic acid enzymes illustrates
             QD{\ensuremath{\pi}} has average errors less than 0.5 kcal/mol,
             whereas the other models compared have errors over an order of
             magnitude greater. Taken together, this makes QD{\ensuremath{\pi}}
             highly attractive as a potential force field model for drug discovery.},
    PMCID =    {PMC9992268},
    doi =      {10.1021/acs.jctc.2c01172},
}


@Article{Zhang_JChemInfModel_2023_v63_p1133,
    author =   {Jintu Zhang and Haotian Zhang and Zhixin Qin and Yu Kang and Xin Hong
             and Tingjun Hou},
    title =    {{Quasiclassical Trajectory Simulation as a Protocol to Build Locally
             Accurate Machine Learning Potentials}},
    journal =  {J. Chem. Inf. Model.},
    year =     2023,
    volume =   63,
    issue =    4,
    pages =    {1133--1142},
    annote =   {Direct trajectory calculations have become increasingly popular in
             recent computational chemistry investigations. However, the exorbitant
             computational cost of ab initio trajectory calculations usually limits
             its application in mechanistic explorations. Recently, machine
             learning-based potential energy surface (ML-PES) provides a powerful
             strategy to circumvent the heavy computational cost and meanwhile
             maintain the required accuracy. Despite the appealing potential,
             constructing a robust ML-PES is still challenging since the training
             set of the PES should cover a broad enough configuration space. In
             this work, we demonstrate that when the concerned properties could be
             collected by the localized sampling of the configuration space,
             quasiclassical trajectory (QCT) calculations can be invoked to
             efficiently obtain locally accurate ML-PESs. We prove our concept with
             two model reactions: methyl migration of i-pentane cation and
             dimerization of cyclopentadiene. We found that the locally accurate
             ML-PESs are sufficiently robust for reproducing the static and dynamic
             features of the reactions, including the time-resolved free energy and
             entropy changes, and time gaps.},
    doi =      {10.1021/acs.jcim.2c01497},
}


@Article{Li_PhysChemChemPhys_2023_v25_p6746,
    author =   {Zhiqiang Li and Xiaoyu Tan and Zhiwei Fu and Linhua Liu and Jia-Yue
             Yang},
    title =    {{Thermal transport across copper{\textendash}water interfaces according
             to deep potential molecular dynamics}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2023,
    volume =   25,
    issue =    9,
    pages =    {6746--6756},
    annote =   {Nanoscale thermal transport at solid-liquid interfaces plays an
             essential role in many engineering fields. This work performs deep
             potential molecular dynamics (DPMD) simulations to investigate thermal
             transport across copper-water interfaces. Unlike traditional classical
             molecular dynamics (CMD) simulations, we independently train a deep
             learning potential (DLP) based on density functional theory (DFT)
             calculations and demonstrated its high computational efficiency and
             accuracy. The trained DLP predicts radial distribution functions
             (RDFs), vibrational densities of states (VDOS), density curves, and
             thermal conductivity of water confined in the nanochannel at a DFT
             accuracy. The thermal conductivity decreases slightly with an increase
             in the channel height, while the influence of the cross-sectional area
             is negligible. Moreover, the predicted interfacial thermal conductance
             (ITC) across the copper-water interface by DPMD is 2.505 {\texttimes}
             108 W m-2 K-1, the same order of magnitude as the CMD and experimental
             results but with a high computational accuracy. This work seeks to
             simulate the thermal transport properties of solid-liquid interfaces
             with DFT accuracy at large-system and long-time scales.},
    doi =      {10.1039/d2cp05530a},
}


@Article{Gomes-Filho_JPhysChemB_2023_v127_p1422,
    author =   {M{\'a}rcio S Gomes-Filho and Alberto Torres and Alexandre {Reily
             Rocha} and Luana S Pedroza},
    title =    {{Size and Quality of Quantum Mechanical Data Set for Training Neural
             Network Force Fields for Liquid Water}},
    journal =  {J. Phys. Chem. B},
    year =     2023,
    volume =   127,
    issue =    6,
    pages =    {1422--1428},
    annote =   {Molecular dynamics simulations have been used in different scientific
             fields to investigate a broad range of physical systems. However, the
             accuracy of calculation is based on the model considered to describe
             the atomic interactions. In particular, ab initio molecular dynamics
             (AIMD) has the accuracy of density functional theory (DFT) and thus is
             limited to small systems and a relatively short simulation time. In
             this scenario, Neural Network Force Fields (NNFFs) have an important
             role, since they provide a way to circumvent these caveats. In this
             work, we investigate NNFFs designed at the level of DFT to describe
             liquid water, focusing on the size and quality of the training data
             set considered. We show that structural properties are less dependent
             on the size of the training data set compared to dynamical ones (such
             as the diffusion coefficient), and a good sampling (selecting data
             reference for the training process) can lead to a small sample with
             good precision.},
    doi =      {10.1021/acs.jpcb.2c09059},
}


@Article{FidalgoCandido_JChemPhys_2023_v158_p064502,
    author =   {Vitor {Fidalgo C{\^a}ndido} and Filipe Matusalem and Maurice {de
             Koning}},
    title =    {{Melting conditions and entropies of superionic water ice: Free-energy
             calculations based on hybrid solid/liquid reference systems}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    6,
    pages =    064502,
    annote =   {Superionic (SI) water ices-high-temperature, high-pressure phases of
             water in which oxygen ions occupy a regular crystal lattice whereas
             the protons flow in a liquid-like manner-have attracted a growing
             amount of attention over the past few years, in particular due to
             their possible role in the magnetic anomalies of the ice giants
             Neptune and Uranus. In this paper, we consider the calculation of the
             free energies of such phases, exploring hybrid reference systems
             consisting of a combination of an Einstein solid for the oxygen ions
             occupying a crystal lattice and a Uhlenbeck-Ford potential for the
             protonic fluid that avoids irregularities associated with possible
             particle overlaps. Applying this approach to a recent neural-network
             potential-energy landscape for SI water ice, we compute Gibbs free
             energies as a function of temperature for the SI fcc and liquid phases
             to determine the melting temperature Tm at 340{~}GPa. The results are
             consistent with previous estimates and indicate that the entropy
             difference between both phases is comparatively small, in particular
             due to the large amplitude of vibration of the oxygen ions in the fcc
             phase at the melting temperature.},
    doi =      {10.1063/5.0138987},
}


@Article{Deng_ComputationalMaterialsScience_2023_v218_p111941,
    author =   {Fenglin Deng and Hongyu Wu and Ri He and Peijun Yang and Zhicheng
             Zhong},
    title =    {{Large-scale atomistic simulation of dislocation core structure in
             face-centered cubic metal with Deep Potential method}},
    journal =  {Computational Materials Science},
    year =     2023,
    volume =   218,
    pages =    111941,
    doi =      {10.1016/j.commatsci.2022.111941},
}


@Article{Yao_RSCAdv_2023_v13_p4565,
    author =   {Songyuan Yao and Richard Van and Xiaoliang Pan and Ji Hwan Park and
             Yuezhi Mao and Jingzhi Pu and Ye Mei and Yihan Shao},
    title =    {{Machine learning based implicit solvent model for aqueous-solution
             alanine dipeptide molecular dynamics simulations}},
    journal =  {RSC Adv.},
    year =     2023,
    volume =   13,
    issue =    7,
    pages =    {4565--4577},
    annote =   {Inspired by the recent work from No{\'e} and coworkers on the
             development of machine learning based implicit solvent model for the
             simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021,
             155, 084101], here we report another investigation of the possibility
             of using machine learning (ML) techniques to "derive" an implicit
             solvent model directly from explicit solvent molecular dynamics (MD)
             simulations. For alanine dipeptide, a machine learning potential (MLP)
             based on the DeepPot-SE representation of the molecule was trained to
             capture its interactions with its average solvent environment
             configuration (ASEC). The predicted forces on the solute deviated only
             by an RMSD of 0.4 kcal mol-1 {\r{A}}-1 from the reference values, and
             the MLP-based free energy surface differed from that obtained from
             explicit solvent MD simulations by an RMSD of less than 0.9 kcal
             mol-1. Our MLP training protocol could also accurately reproduce
             combined quantum mechanical molecular mechanical (QM/MM) forces on the
             quantum mechanical (QM) solute in ASEC environment, thus enabling the
             development of accurate ML-based implicit solvent models for ab
             initio-QM MD simulations. Such ML-based implicit solvent models for QM
             calculations are cost-effective in both the training stage, where the
             use of ASEC reduces the number of data points to be labelled, and the
             inference stage, where the MLP can be evaluated at a relatively small
             additional cost on top of the QM calculation of the solute.},
    PMCID =    {PMC9900604},
    doi =      {10.1039/d2ra08180f},
}


@Article{Deng_PhysRevB_2023_v107_p064103,
    author =   {Jie Deng and Haiyang Niu and Junwei Hu and Mingyi Chen and Lars
             Stixrude},
    title =    {{Melting of  MgSiO3
             determined by machine learning potentials}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   107,
    issue =    6,
    pages =    064103,
    doi =      {10.1103/PhysRevB.107.064103},
}


@Article{Sours_JPhysChemCNanomaterInterfaces_2023_v127_p1455,
    author =   {Tyler G Sours and Ambarish R Kulkarni},
    title =    {{Predicting Structural Properties of Pure Silica Zeolites Using Deep
             Neural Network Potentials}},
    journal =  {J. Phys. Chem. C. Nanomater. Interfaces},
    year =     2023,
    volume =   127,
    issue =    3,
    pages =    {1455--1463},
    annote =   {Machine learning potentials (MLPs) capable of accurately describing
             complex ab initio potential energy surfaces (PESs) have revolutionized
             the field of multiscale atomistic modeling. In this work, using an
             extensive density functional theory (DFT) data set (denoted as Si-
             ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT
             calculations) found in the International Zeolite Association (IZA)
             database, we have trained a DeePMD-kit MLP to model the dynamics of
             silica frameworks. The performance of our model is evaluated by
             calculating various properties that probe the accuracy of the energy
             and force predictions. This MLP demonstrates impressive agreement with
             DFT for predicting zeolite structural properties, energy-volume
             trends, and phonon density of states. Furthermore, our model achieves
             reasonable predictions for stress-strain relationships without
             including DFT stress data during training. These results highlight the
             ability of MLPs to capture the flexibility of zeolite frameworks and
             motivate further MLP development for nanoporous materials with near-ab
             initio accuracy.},
    PMCID =    {PMC9885523},
    doi =      {10.1021/acs.jpcc.2c08429},
}


@Article{Zhang_PhysChemChemPhys_2023_v25_p6164,
    author =   {Pan Zhang and Mi Qin and Zhenhua Zhang and Dan Jin and Yong Liu and
             Ziyu Wang and Zhihong Lu and Jing Shi and Rui Xiong},
    title =    {{Accessing the thermal conductivities of Sb<sub>2</sub>Te<sub>3</sub>
             and Bi<sub>2</sub>Te<sub>3</sub>/Sb<sub>2</sub>Te<sub>3</sub>
             superlattices by molecular dynamics simulations with a deep neural
             network potential}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2023,
    volume =   25,
    issue =    8,
    pages =    {6164--6174},
    annote =   {Phonon thermal transport is a key feature for the operation of
             thermoelectric materials, but it is challenging to accurately
             calculate the thermal conductivity of materials with strong
             anharmonicity or large cells. In this work, a deep neural network
             potential (NNP) is developed using a dataset based on density
             functional theory (DFT) and applied to describe the lattice dynamics
             of Sb2Te3 and Bi2Te3/Sb2Te3 superlattices. The lattice thermal
             conductivities of Sb2Te3 are first predicted using equilibrium
             molecular dynamics (EMD) simulations combined with an NNP and the
             results match well with experimental values. Then, through further
             exploration of weighted phase spaces and the Gr{\"u}neisen parameter,
             we find that there is a stronger anharmonicity in the out-of-plane
             direction in Sb2Te3, which is the reason why the thermal
             conductivities are overestimated more in the out-of-plane direction
             than in the in-plane direction by solving the phonon Boltzmann
             transport equation (BTE) with only three-phonon scattering processes
             being considered. More importantly, the lattice thermal conductivities
             of Bi2Te3/Sb2Te3 superlattices with different periods are accurately
             predicted using non-equilibrium molecular dynamics (NEMD) simulations
             together with an NNP, which serves as a good example to explore the
             thermal transport physics of superlattices using a deep neural network
             potential.},
    doi =      {10.1039/d2cp05590b},
}


@Article{Xu_Unknown_2023_v122_pNone,
    author =   {Xiong Xu and Fangbiao Li and Chang Niu and Min Li and Hui Wang},
    title =    {{Machine learning assisted investigation of the barocaloric performance
             in ammonium iodide}},
    year =     2023,
    volume =   122,
    issue =    4,
    annote =   {<jats:p>Using the ab initio-based training database, we trained the
             potential function for ammonium iodide (NH4I) based on a deep neural
             network-based model. On the basis of this potential function, we
             simulated the temperature-driven {\ensuremath{\beta}}{\,}{\ensuremath{
             \Rightarrow}}{\,}{\ensuremath{\alpha}}-phase transition of NH4I with
             isobaric isothermal ensemble via molecular dynamics simulations, the
             results of which are in good agreement with recent experimental
             results. As it increases near the phase transition temperature, a
             quarter of ionic bonds of NH4+-I{\ensuremath{-}} break so that NH4+
             starts to rotate randomly in a disorderly manner, being able to store
             thermal energy without a temperature rise. It is found that NH4I
             possesses a giant isothermal entropy change ({\ensuremath{\sim}}93{\,}
             J{\,}K{\ensuremath{-}}1{\,}kg{\ensuremath{-}}1) and adiabatic
             temperature ({\ensuremath{\sim}}27{\,}K) at low driving pressure
             ({\ensuremath{\sim}}10{\,}MPa). In addition, through partial
             substitution of I by Br in NH4I, it is found that the thermal
             conductivity can be remarkably improved, ascribed to the enhancement
             of lifetime of low frequency phonons contributed by bromine and
             iodine. The present work provides a method and important guidance for
             the future exploration and design of barocaloric material for
             practical applications.</jats:p>},
    doi =      {10.1063/5.0131696},
}


@Article{Wisesa_JPhysChemLett_2023_v14_p468,
    author =   {Pandu Wisesa and Christopher M Andolina and Wissam A Saidi},
    title =    {{Development and Validation of Versatile Deep Atomistic Potentials for
             Metal Oxides}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    2,
    pages =    {468--475},
    annote =   {Machine learning interatomic potentials powered by neural networks
             have been shown to readily model a gradient of compositions in
             metallic systems. However, their application to date on ionic systems
             tends to focus on specific compositions and oxidation states owing to
             their more heterogeneous chemical nature. Herein we show that a deep
             neural network potential (DNP) can model various properties of metal
             oxides with different oxidation states without additional charge
             information. We created and validated DNPs for AgxOy, CuxOy MgxOy,
             PtxOy, and ZnxOy, whereby each system was trained without any
             limitations on oxidation states. We illustrate how the database can be
             augmented to enhance the DNP transferability for a new polymorph,
             surface energies, and thermal expansion. In addition, we show that
             these potentials can correctly interpolate significant pressure and
             temperature ranges, exhibit stability over long molecular dynamics
             simulation time scales, and replicate nonharmonic thermal expansion,
             consistent with experimental results.},
    doi =      {10.1021/acs.jpclett.2c03445},
}


@Article{Panagiotopoulos_JPhysChemB_2023_v127_p430,
    author =   {Athanassios Z Panagiotopoulos and Shuwen Yue},
    title =    {{Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations}},
    journal =  {J. Phys. Chem. B},
    year =     2023,
    volume =   127,
    issue =    2,
    pages =    {430--437},
    annote =   {This Perspective article focuses on recent simulation work on the
             dynamics of aqueous electrolytes. It is well-established that full-
             charge, nonpolarizable models for water and ions generally predict
             solution dynamics that are too slow in comparison to experiments.
             Models with reduced (scaled) charges do better for solution
             diffusivities and viscosities but encounter issues describing other
             dynamic phenomena such as nucleation rates of crystals from solution.
             Polarizable models show promise, especially when appropriately
             parametrized, but may still miss important physical effects such as
             charge transfer. First-principles calculations are starting to emerge
             for these properties that are in principle able to capture
             polarization, charge transfer, and chemical transformations in
             solution. While direct ab initio simulations are still too slow for
             simulations of large systems over long time scales, machine-learning
             models trained on appropriate first-principles data show significant
             promise for accurate and transferable modeling of electrolyte solution
             dynamics.},
    doi =      {10.1021/acs.jpcb.2c07477},
}


@Article{Wen_ProcNatlAcadSciUSA_2023_v120_pe2212250120,
    author =   {Bo Wen and Marcos F {Calegari Andrade} and Li-Min Liu and Annabella
             Selloni},
    title =    {{Water dissociation at the water{\textendash}rutile TiO
             <sub>2</sub>             (110) interface from ab{~}initio-based deep
             neural network simulations}},
    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
    year =     2023,
    volume =   120,
    issue =    2,
    pages =    {e2212250120},
    annote =   {The interaction of water with TiO2 surfaces is of crucial importance
             in various scientific fields and applications, from photocatalysis for
             hydrogen production and the photooxidation of organic pollutants to
             self-cleaning surfaces and bio-medical devices. In particular, the
             equilibrium fraction of water dissociation at the TiO2-water interface
             has a critical role in the surface chemistry of TiO2, but is difficult
             to determine both experimentally and computationally. Among TiO2
             surfaces, rutile TiO2(110) is of special interest as the most abundant
             surface of TiO2's stable rutile phase. While surface-science studies
             have provided detailed information on the interaction of rutile
             TiO2(110) with gas-phase water, much less is known about the
             TiO2(110)-water interface, which is more relevant to many
             applications. In this work, we characterize the structure of the
             aqueous TiO2(110) interface using nanosecond timescale molecular
             dynamics simulations with ab{~}initio-based deep neural network
             potentials that accurately describe water/TiO2(110) interactions over
             a wide range of water coverages. Simulations on TiO2(110) slab models
             of increasing thickness provide insight into the dynamic equilibrium
             between molecular and dissociated adsorbed water at the interface and
             allow us to obtain a reliable estimate of the equilibrium fraction of
             water dissociation. We find a dissociation fraction of 22
             {\ensuremath{\pm}} 6% with an associated average hydroxyl lifetime of
             7.6 {\ensuremath{\pm}} 1.8 ns. These quantities are both much larger
             than corresponding estimates for the aqueous anatase TiO2(101)
             interface, consistent with the higher water photooxidation activity
             that is observed for rutile relative to anatase.},
    PMCID =    {PMC9926290},
    doi =      {10.1073/pnas.2212250120},
}


@Article{Jin_JChemTheoryComput_2023_v19_p7343,
    author =   {Bin Jin and Taiping Hu and Kuang Yu and Shenzhen Xu},
    title =    {{Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical
             Reaction Simulations}},
    journal =  {J. Chem. Theory Comput.},
    year =     2023,
    volume =   19,
    issue =    20,
    pages =    {7343--7357},
    annote =   {Most electrochemical reactions should be studied under a grand
             canonical ensemble condition with a constant potential and/or a
             constant pH value. Free energy profiles provide key insights into
             understanding the reaction mechanisms. However, many molecular
             dynamics (MD)-based theoretical studies for electrochemical reactions
             did not employ an exact grand canonical ensemble sampling scheme for
             the free energy calculations, partially due to the issues of
             discontinuous trajectories induced by the particle-number variations
             during MD simulations. An alternative statistical sampling approach,
             the Monte Carlo (MC) method, is naturally appropriate for the open-
             system simulations if we focus on the thermodynamic properties. An
             advanced MC scheme, the hybrid Monte Carlo (HMC) method, which can
             efficiently sample the configurations of a system with large degrees
             of freedom, however, has limitations in the constrained-sampling
             applications. In this work, we propose an adjusted constrained HMC
             method to compute free energy profiles using the thermodynamic
             integration (TI) method. The key idea of the method for handling the
             constraint in TI is to integrate the reaction coordinate and sample
             the rest degrees of freedom by two types of MC schemes, the HMC scheme
             and the Metropolis algorithm with unbiased trials (M(RT)2-UB). We test
             the proposed method on three different systems involving two kinds of
             reaction coordinates, which are the distance between two particles and
             the difference of particles' distances, and compare the results to
             those generated by the constrained M(RT)2-UB method serving as
             benchmarks. We show that our proposed method has the advantages of
             high sampling efficiency and convenience of implementation, and the
             accuracy is justified as well. In addition, we show in the third test
             system that the proposed constrained HMC method can be combined with
             the path integral method to consider the nuclear quantum effects,
             indicating a broader application scenario of the sampling method
             reported in this work.},
    doi =      {10.1021/acs.jctc.3c00571},
}


@Article{Zhao_JMaterChemA_2023_v11_p23999,
    author =   {Jia Zhao and Taixi Feng and Guimin Lu and Jianguo Yu},
    title =    {{Insights into the local structure evolution and thermophysical
             properties of NaCl{\textendash}KCl{\textendash}MgCl<sub>2</sub>{\texte
             ndash}LaCl<sub>3</sub> melt driven by machine learning}},
    journal =  {J. Mater. Chem. A},
    year =     2023,
    volume =   11,
    issue =    44,
    pages =    {23999--24012},
    annote =   {<jats:p>The local structure evolution and thermophysical properties of
             the NaCl{\textendash}KCl{\textendash}MgCl<jats:sub>2</jats:sub>{\texte
             ndash}LaCl<jats:sub>3</jats:sub> melt were thoroughly understood,
             which facilitates the advancement and innovation of molten salt
             electrolytic production for Mg{\textendash}La alloys.</jats:p>},
    doi =      {10.1039/d3ta03434h},
}


@Article{Avula_JPhysChemLett_2023_v14_p9500,
    author =   {Nikhil V S Avula and Michael L Klein and Sundaram Balasubramanian},
    title =    {{Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes
             Using Machine Learned Potentials}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    issue =    42,
    pages =    {9500--9507},
    annote =   {The diffusivity of water in aqueous cesium iodide solutions is larger
             than that in neat liquid water and vice versa for sodium chloride
             solutions. Such peculiar ion-specific behavior, called anomalous
             diffusion, is not reproduced in typical force field based molecular
             dynamics (MD) simulations due to inadequate treatment of ion-water
             interactions. Herein, this hurdle is tackled by using machine learned
             atomic potentials (MLPs) trained on data from density functional
             theory calculations. MLP based atomistic MD simulations of aqueous
             salt solutions reproduce experimentally determined thermodynamic,
             structural, dynamical, and transport properties, including their
             varied trends in water diffusivities across salt concentration. This
             enables an examination of their intermolecular structure to unravel
             the microscopic underpinnings of the differences in their transport
             properties. While both ions in CsI solutions contribute to the faster
             diffusion of water molecules, the competition between the heavy
             retardation by Na ions and the slight acceleration by Cl ions in NaCl
             solutions reduces their water diffusivity.},
    doi =      {10.1021/acs.jpclett.3c02112},
}


@Article{Muniz_JPhysChemB_2023_v127_p9165,
    author =   {Maria Carolina Muniz and Roberto Car and Athanassios Z Panagiotopoulos},
    title =    {{Neural Network Water Model Based on the MB-Pol Many-Body Potential}},
    journal =  {J. Phys. Chem. B},
    year =     2023,
    volume =   127,
    issue =    42,
    pages =    {9165--9171},
    annote =   {The MB-pol many-body potential accurately predicts many properties of
             water, including cluster, liquid phase, and vapor-liquid equilibrium
             properties, but its high computational cost can make applying it in
             large-scale simulations quite challenging. In order to address this
             limitation, we developed a "deep potential" neural network (DPMD)
             model based on the MB-pol potential for water. We find that a DPMD
             model trained on mostly liquid configurations yields a good
             description of the bulk liquid phase but severely underpredicts vapor-
             liquid coexistence densities. By contrast, adding cluster
             configurations to the neural network training set leads to a good
             agreement for the vapor coexistence densities. Liquid phase densities
             under supercooled conditions are also represented well, even though
             they were not included in the training set. These results confirm that
             neural network models can combine accuracy and transferability if
             sufficient attention is given to the construction of a representative
             training set for the target system.},
    doi =      {10.1021/acs.jpcb.3c04629},
}


@Article{Zhang_JPhysChemB_2023_v127_p8926,
    author =   {Wei Zhang and Li Zhou and Tinggui Yan and Mohan Chen},
    title =    {{Speciation of La<sup>3+</sup>{\textendash}Cl<sup>{\textendash}</sup>
             Complexes in Hydrothermal Fluids from Deep Potential Molecular
             Dynamics}},
    journal =  {J. Phys. Chem. B},
    year =     2023,
    volume =   127,
    issue =    41,
    pages =    {8926--8937},
    annote =   {The stability of rare earth element (REE) complexes plays a crucial
             role in quantitatively assessing their hydrothermal migration and
             transformation. However, reliable data are lacking under high-
             temperature hydrothermal conditions, which hampers our understanding
             of the association behavior of REE. Here a deep learning potential
             model for the LaCl3-H2O system in hydrothermal fluids is developed
             based on the first-principles density functional theory calculations.
             The model accurately predicts the radial distribution functions
             compared to ab initio molecular dynamics (AIMD) simulations.
             Furthermore, species of La-Cl complexes, the dissociation pathway of
             the La-Cl complexes dissociation process, and the potential of mean
             forces and corresponding association constants (logK) for LaCln3-n (n
             = 1-4) are extensively investigated under a wide range of temperatures
             and pressures. Empirical density models for logK calculation are
             fitted with these data and can accurately predict logK data from both
             experimental results and AIMD simulations. The distribution of La-Cl
             species is also evaluated across a wide range of temperatures,
             pressures, and initial chloride concentration conditions. The results
             show that La-Cl complexes are prone to forming in a low-density
             solution, and the number of bonded Cl- ions increases with rising
             temperature. In contrast, in a high-density solution, La3+ dominates
             and becomes the more prevalent species.},
    doi =      {10.1021/acs.jpcb.3c05428},
}


@Article{Xia_JMaterChemA_2023_vNone_pNone,
    author =   {Weiyi Xia and Ling Tang and Huaijun Sun and Chao Zhang and Kai-Ming Ho
             and Gayatri Viswanathan and Kirill Kovnir and Cai-Zhuang Wang},
    title =    {{Accelerating materials discovery using integrated deep machine
             learning approaches}},
    journal =  {J. Mater. Chem. A},
    year =     2023,
    annote =   {<jats:p>Our work introduces an innovative deep machine learning
             framework to significantly accelerate novel materials discovery, as
             demonstrated by its application to the La{\textendash}Si{\textendash}P
             system where new ternary and quaternary compounds were successfully
             identified.</jats:p>},
    doi =      {10.1039/d3ta03771a},
}


@Article{Piaggi_FaradayDiscuss_2023_vNone_pNone,
    author =   {Pablo M Piaggi and Annabella Selloni and Athanassios Z Panagiotopoulos
             and Roberto Car and Pablo G Debenedetti},
    title =    {{A first-principles machine-learning force field for heterogeneous ice
             nucleation on microcline feldspar}},
    journal =  {Faraday Discuss.},
    year =     2023,
    annote =   {The formation of ice in the atmosphere affects precipitation and cloud
             properties, and plays a key role in the climate of our planet.
             Although ice can form directly from liquid water under deeply
             supercooled conditions, the presence of foreign particles can aid ice
             formation at much warmer temperatures. Over the past decade,
             experiments have highlighted the remarkable efficiency of feldspar
             minerals as ice nuclei compared to other particles present in the
             atmosphere. However, the exact mechanism of ice formation on feldspar
             surfaces has yet to be fully understood. Here, we develop a first-
             principles machine-learning model for the potential energy surface
             aimed at studying ice nucleation at microcline feldspar surfaces. The
             model is able to reproduce with high-fidelity the energies and forces
             derived from density-functional theory (DFT) based on the SCAN
             exchange and correlation functional. Our training set includes
             configurations of bulk supercooled water, hexagonal and cubic ice,
             microcline, and fully-hydroxylated feldspar surfaces exposed to a
             vacuum, liquid water, and ice. We apply the machine-learning force
             field to study different fully-hydroxylated terminations of the (100),
             (010), and (001) surfaces of microcline exposed to a vacuum. Our
             calculations suggest that terminations that do not minimize the number
             of broken bonds are preferred in a vacuum. We also study the structure
             of supercooled liquid water in contact with microcline surfaces, and
             find that water density correlations extend up to around 10 {\r{A}}
             from the surfaces. Finally, we show that the force field maintains a
             high accuracy during the simulation of ice formation at microcline
             surfaces, even for large systems of around 30{\,}000 atoms. Future
             work will be directed towards the calculation of nucleation free-
             energy barriers and rates using the force field developed herein, and
             understanding the role of different microcline surfaces in ice
             nucleation.},
    doi =      {10.1039/d3fd00100h},
}

@article{Gegentana_Ionics_2023,
  title={A deep potential molecular dynamics study on the ionic structure and transport properties of NaCl-CaCl2 molten salt},
  author={Gegentana and Cui, Liu and Zhou, Leping and Du, Xiaoze},
  journal={Ionics},
  pages={1--11},
  year={2023},
  publisher={Springer}
}

@Article{Li_JournaloftheEuropeanCeramicSociety_2024_v44_p659,
    author =   {Jun Li and Kun Luo and Qi An},
    title =    {{Mobile dislocation mediated Hall-Petch and inverse Hall-Petch
             behaviors in nanocrystalline Al-doped boron carbide}},
    journal =  {Journal of the European Ceramic Society},
    year =     2024,
    volume =   44,
    issue =    2,
    pages =    {659--667},
    doi =      {10.1016/j.jeurceramsoc.2023.09.079},
}


@Article{Achar_JournalofMaterialsResearch_2023_vNone_pNone,
    author =   {Siddarth K. Achar and Leonardo Bernasconi and Juan J. Alvarez and J.
             Karl Johnson},
    title =    {{Deep-learning potentials for proton transport in double-sided
             graphanol}},
    journal =  {Journal of Materials Research},
    year =     2023,
    doi =      {10.1557/s43578-023-01141-3},
}


@Article{Ding_Tungsten_2023_vNone_pNone,
    author =   {Chang-Jie Ding and Ya-Wei Lei and Xiao-Yang Wang and Xiao-Lin Li and
             Xiang-Yan Li and Yan-Ge Zhang and Yi-Chun Xu and Chang-Song Liu and
             Xue-Bang Wu},
    title =    {{A deep learning interatomic potential suitable for simulating
             radiation damage in bulk tungsten}},
    journal =  {Tungsten},
    year =     2023,
    doi =      {10.1007/s42864-023-00230-4},
}


@Article{Zhang_PhysChemChemPhys_2023_v25_p15422,
    author =   {Pan Zhang and Wenkai Liao and Ziyang Zhu and Mi Qin and Zhenhua Zhang
             and Dan Jin and Yong Liu and Ziyu Wang and Zhihong Lu and Rui Xiong},
    title =    {{Tuning the lattice thermal conductivity of
             Sb<sub>2</sub>Te<sub>3</sub> by Cr doping: a deep potential molecular
             dynamics study}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2023,
    volume =   25,
    issue =    22,
    pages =    {15422--15432},
    annote =   {Element doping is a prominent method for reducing the lattice thermal
             conductivity and optimizing the thermoelectric performance of
             materials in the thermoelectric field. However, determination of the
             thermal conductivity of element-doped systems is a challenging task,
             especially when the elements are randomly doped. In this work, a
             first-principles based deep neural network potential (NNP) is
             developed to investigate the lattice thermal transport properties of
             Cr-doped Sb2Te3 using molecular dynamics simulations. Compared with
             pure Sb2Te3, the thermal conductivity of orderly Cr-doped Sb2Te3 with
             Cr atoms locating at specific atomic layer positions decreases
             slightly in the in-plane direction, but sharply in the out-of-plane
             direction. The decrease of the low frequency phonon density of states
             and the enhancement of phonon scattering near 2.5 THz are the primary
             reasons for the decrease in the thermal conductivity of Cr-doped
             Sb2Te3, while the decrease of phonon velocity due to band flattening
             is the reason for the sharp decrease of thermal conductivity in the
             out-of-plane direction. Moreover, the thermal conductivities of
             randomly Cr-doped Sb2Te3 with different Cr concentrations are also
             investigated using the NNP. It is found that the thermal
             conductivities in both the in-plane and out-of-plane directions are
             reduced by 76% and 80%, respectively, for Sb36Cr36Te108. Furthermore,
             the influence of different Cr dopant arrays on the thermal
             conductivity of Sb2Te3 is also predicted using the NNP. Our work
             provides a good example for predicting the thermal conductivity of
             element-doped systems using the NNP combined with molecular dynamics
             simulations.},
    doi =      {10.1039/d3cp00999h},
}


@Article{Li_JournalofSustainableCementBasedMaterials_2023_v12_p1335,
    author =   {Weihuan Li and Yang Zhou and Li Ding and Pengfei Lv and Yifan Su and
             Rui Wang and Changwen Miao},
    title =    {{A deep learning-based potential developed for calcium silicate
             hydrates with both high accuracy and efficiency}},
    journal =  {Journal of Sustainable Cement-Based Materials},
    year =     2023,
    volume =   12,
    issue =    11,
    pages =    {1335--1346},
    doi =      {10.1080/21650373.2023.2219251},
}


@Article{Zhang_PhysChemChemPhys_2023_v25_p13297,
    author =   {Zhou Zhang and Zhongyun Ma and Yong Pei},
    title =    {{Li ion diffusion behavior of Li<sub>3</sub>OCl solid-state
             electrolytes with different defect structures: insights from the deep
             potential model}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2023,
    volume =   25,
    issue =    19,
    pages =    {13297--13307},
    annote =   {Li3OX (X = Cl, Br), a lithium-rich anti-perovskite material developed
             in recent years, has received tremendous attention due to its high
             ionic conductivity of >10-3 S cm-1 at room temperature. However, the
             origin of the high ionic conductivity of the material at the atomic
             level is still not clear. In this work, we investigated the dynamic
             behavior of the Li3OCl system with three different defect structures
             (Li-Frenkel, LiCl-Schottky, and Cl-O anti-site disorder) at seven
             temperature intervals and calculated its ionic conductivity using the
             deep potential (DP) model. The results show that the presence of LiCl-
             Schottky defects is the main reason for the high performance of the
             Li3OCl system, and the Li vacancy is the main carrier. The ionic
             conductivity obtained from the DP model is 0.49 {\texttimes} 10-3 S
             cm-1 at room temperature and it can reach 10-2 S cm-1 above the
             melting point, which is in the same order of magnitude as the
             experimentally reported results. We also explored the effect of
             different defect concentrations on the ionic conductivity and
             migration activation energy. This work also demonstrates the
             feasibility of the DP method for solving the accuracy-efficiency
             dilemma of ab initio molecular dynamics (AIMD) and classical molecular
             dynamics simulations.},
    doi =      {10.1039/d2cp06073f},
}


@Article{Thong_PhysRevB_2023_v107_p014101,
    author =   {Hao-Cheng Thong and XiaoYang Wang and Jian Han and Linfeng Zhang and
             Bei Li and Ke Wang and Ben Xu},
    title =    {{Machine learning interatomic potential for molecular dynamics
             simulation of the ferroelectric  KNbO3  perovskite}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   107,
    issue =    1,
    pages =    014101,
    doi =      {10.1103/PhysRevB.107.014101},
}


@Article{Shang_Fuel_2024_v357_p129909,
    author =   {Zhe Shang and Hui Li},
    title =    {{Unraveling pyrolysis mechanisms of lignin dimer model compounds:
             Neural network-based molecular dynamics simulation investigations}},
    journal =  {Fuel},
    year =     2024,
    volume =   357,
    pages =    129909,
    doi =      {10.1016/j.fuel.2023.129909},
}


@Article{He_ActaMaterialia_2024_v262_p119416,
    author =   {Ri He and Bingwen Zhang and Hua Wang and Lei Li and Ping Tang and
             Gerrit Bauer and Zhicheng Zhong},
    title =    {{Ultrafast switching dynamics of the ferroelectric order in stacking-
             engineered ferroelectrics}},
    journal =  {Acta Materialia},
    year =     2024,
    volume =   262,
    pages =    119416,
    doi =      {10.1016/j.actamat.2023.119416},
}

@Article{Marcolongo_Chemsystemschem_2020_v2,
    author =   {Aris Marcolongo and Tobias Binninger and Federico Zipoli and Teodoro
             Laino},
    title =    {{Simulating Diffusion Properties of Solid{-}State Electrolytes via a
             Neural Network Potential: Performance and Training Scheme}},
    journal =  {Chemsystemschem},
    year =     2020,
    volume =   2,
    number =   3,
    doi =      {10.1002/syst.201900031},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>The
             recently published DeePMD model, based on a deep neural network
             architecture, brings the hope of solving the time{-}scale issue which
             often prevents the application of first principle molecular dynamics
             to physical systems. With this contribution we assess the performance
             of the DeePMD potential on a real{-}life application and model
             diffusion of ions in solid{-}state electrolytes. We consider as test
             cases the well known Li<jats:sub>10{\ens
             uremath{<}}/jats:sub>GeP<jats:sub{\ensur
             emath{>}}2</jats:sub>S<ja
             ts:sub>12</jats:sub>, Li{
             \ensuremath{<}}jats:sub>7</jats:sub{\ens
             uremath{>}}La<jats:sub>3<
             /jats:sub>Zr<jats:sub>2{\
             ensuremath{<}}/jats:sub>O<jats:sub{\ensu
             remath{>}}12</jats:sub> and Na{\ensurema
             th{<}}jats:sub>3</jats:sub{\ensuremath{>
             }}Zr<jats:sub>2</jats:sub
             >Si<jats:sub>2{\ensuremat
             h{<}}/jats:sub>PO<jats:sub{\ensuremath{>
             }}12</jats:sub>. We develop and test a
             training protocol suitable for the computation of diffusion
             coefficients, which is one of the key properties to be optimized for
             battery applications, and we find good agreement with previous
             computations. Our results show that the DeePMD model may be a
             successful component of a framework to identify novel solid{-}state
             electrolytes.</jats:p>},
}

@Article{Xu_JPhysChemC_2023_v127_p24106,
    author =   {Longkun Xu and Wei Shao and Haishun Jin and Qiang Wang},
    title =    {{Data Efficient and Stability Indicated Sampling for Developing
             Reactive Machine Learning Potential to Achieve Ultralong Simulation in
             Lithium-Metal Batteries}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    number =   50,
    pages =    {24106--24117},
    doi =      {10.1021/acs.jpcc.3c05522},
}

@Article{Wu_JPhysChemLett_2023_v14_p11465,
    author =   {Yu Wu and Ying Chen and Zhenxing Fang and Yimin Ding and Qiaoqiao Li
             and Kui Xue and Hezhu Shao and Hao Zhang and Liujiang Zhou},
    title =    {{Ultralow Lattice Thermal Transport and Considerable Wave-like Phonon
             Tunneling in Chalcogenide Perovskite BaZrS3}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2023,
    volume =   14,
    number =   50,
    pages =    {11465--11473},
    doi =      {10.1021/acs.jpclett.3c02940},
    abstract = {Chalcogenide perovskites provide a promising avenue for nontoxic,
             stable thermoelectric materials. Here, the thermal transport and
             thermoelectric properties of BaZrS3 as a typical orthorhombic
             perovskite are investigated. An extremely low lattice thermal
             conductivity {\ensuremath{\kappa}}L of 1.84 W/mK at 300 K is revealed
             for BaZrS3, due to the softening effect of Ba atoms on the lattice and
             the strong anharmonicity caused by the twisted structure. We
             demonstrate that coherence contributions to {\ensuremath{\kappa}}L,
             arising from wave-like phonon tunneling, lead to an 18{\%} thermal
             transport contribution at 300 K. The increasing temperature softens
             the phonons, thus reducing the group velocity of materials and
             increasing the scattering phase space. However, it simultaneously
             reduces the anharmonicity, which is dominant in BaZrS3 and ultimately
             improves the particle-like thermal transport. In addition, via
             replacement of the S atom with Se- and Ti-alloying strategy, the ZT
             value of BaZrS3 is significantly increased from 0.58 to 0.91 at 500 K,
             making it an important candidate for thermoelectric applications.},
}

@Article{Hu_CellRepPhysSci_2023_v4_p101713,
    author =   {Zheng Hu and Hui Li and Wenbo Zhao and Wei Zhou and Shi Hu},
    title =    {{Microstructure determination of PdRu immiscible alloys based on
             electron-pair distribution function and local elemental segregation}},
    journal =  {Cell Rep. Phys. Sci.},
    year =     2023,
    volume =   4,
    number =   12,
    pages =    101713,
    doi =      {10.1016/j.xcrp.2023.101713},
}

@Article{Loose_JPhysChemB_2023_v127_p10564,
    author =   {Timothy D. Loose and Patrick G. Sahrmann and Thomas S. Qu and Gregory
             A. Voth},
    title =    {{Coarse-Graining with Equivariant Neural Networks: A Path Toward
             Accurate and Data-Efficient Models}},
    journal =  {J. Phys. Chem., B},
    year =     2023,
    volume =   127,
    number =   49,
    pages =    {10564--10572},
    doi =      {10.1021/acs.jpcb.3c05928},
    abstract = {Machine learning has recently entered into the mainstream of coarse-
             grained (CG) molecular modeling and simulation. While a variety of
             methods for incorporating deep learning into these models exist, many
             of them involve training neural networks to act directly as the CG
             force field. This has several benefits of which the most significant
             is accuracy. Neural networks can inherently incorporate multibody
             effects during the calculation of CG forces, and a well-trained neural
             network force field outperforms pairwise basis sets generated from
             essentially any methodology. However, this comes at a significant
             cost. First, these models are typically slower than pairwise force
             fields, even when accounting for specialized hardware, which
             accelerates the training and integration of such networks. The second
             and the focus of this paper is the need for a considerable amount of
             data to train such force fields. It is common to use 10s of
             microseconds of molecular dynamics data to train a single CG model,
             which approaches the point of eliminating the CG model's usefulness in
             the first place. As we investigate in this work, this {''}data-
             hunger{''} trap from neural networks for predicting molecular energies
             and forces can be remediated in part by incorporating equivariant
             convolutional operations. We demonstrate that, for CG water, networks
             that incorporate equivariant convolutional operations can produce
             functional models using data sets as small as a single frame of
             reference data, while networks without these operations cannot.},
}

@Article{Bai_NanoLett_2023_v23_p10922,
    author =   {Dongyu Bai and Yihan Nie and Jing Shang and Junxian Liu and Minghao
             Liu and Yang Yang and Haifei Zhan and Liangzhi Kou and Yuantong Gu},
    title =    {{Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First-
             Principles and Deep Learning Molecular Dynamics Simulations}},
    journal =  {Nano Lett.},
    year =     2023,
    volume =   23,
    number =   23,
    pages =    {10922--10929},
    doi =      {10.1021/acs.nanolett.3c03160},
    abstract = {Despite its prevalence in experiments, the influence of complex strain
             on material properties remains understudied due to the lack of
             effective simulation methods. Here, the effects of bending, rippling,
             and bubbling on the ferroelectric domains are investigated in an
             In2Se3 monolayer by density functional theory and deep learning
             molecular dynamics simulations. Since the ferroelectric switching
             barrier can be increased (decreased) by tensile (compressive) strain,
             automatic polarization reversal occurs in {\ensuremath{\alpha}}-In2Se3
             with a strain gradient when it is subjected to bending, rippling, or
             bubbling deformations to create localized ferroelectric domains with
             varying sizes. The switching dynamics depends on the magnitude of
             curvature and temperature, following an Arrhenius-style relationship.
             This study not only provides a promising solution for cross-scale
             studies using deep learning but also reveals the potential to
             manipulate local polarization in ferroelectric materials through
             strain engineering.},
}

@Article{Shayestehpour_JChemTheoryComput_2023_v19_p8732,
    author =   {Omid Shayestehpour and Stefan Zahn},
    title =    {{Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents
             with First-Principles Accuracy Using Machine Learning Interatomic
             Potentials}},
    journal =  {J. Chem. Theory Comput.},
    year =     2023,
    volume =   19,
    number =   23,
    pages =    {8732--8742},
    doi =      {10.1021/acs.jctc.3c00944},
    abstract = {In recent years, deep eutectic solvents emerged as highly tunable and
             ecofriendly alternatives to common organic solvents and liquid
             electrolytes. In the present work, the ability of machine learning
             (ML) interatomic potentials for molecular dynamics (MD) simulations of
             these liquids is explored, showcasing a trained neural network
             potential for a 1:2 ratio mixture of choline chloride and urea
             (reline). Using the ML potentials trained on density functional theory
             data, MD simulations for large systems of thousands of atoms and
             nanosecond-long time scales are feasible at a fraction of the
             computational cost of the target first-principles simulations. The
             obtained structural and dynamical properties of reline from MD
             simulations using our machine learning models are in good agreement
             with the first-principles MD simulations and experimental results.
             Running a single MD simulation is highlighted as a general shortcoming
             of typical first-principles studies if the dynamic properties are
             investigated. Furthermore, velocity cross-correlation functions are
             employed to study the collective dynamics of the molecular components
             in reline.},
}

@Article{He_PhysRevB_2023_v108_p224114,
    author =   {Ri He and Hongyu Wu and Xuejian Qin and Xuejiao Chen and Zhicheng
             Zhong},
    title =    {{Pressure-induced one-dimensional oxygen ion diffusion channel in lead
             apatite}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   108,
    number =   22,
    pages =    224114,
    doi =      {10.1103/PhysRevB.108.224114},
    abstract = {Recently, Lee et al. claimed that the experimental observation of
             room-temperature ambient-pressure superconductivity in a Cu-doped
             lead-apatite (Pb10-xCux(PO4)6O). The study revealed the Cu doping
             induces a chemical pressure, resulting in a structural contraction of
             one-dimensional Cu-O-Cu atomic column. This unique structure promotes
             a one-dimensional electronic conduction channel along the c-axis
             mediated by the O atoms, which may be related to superconductivity.
             These O atoms occupy 1/4 of the equivalent positions along the c-axis
             and exhibit a low diffusion activation energy of 0.8 eV, indicating
             the possibility of diffusion between these equivalent positions. Here,
             using machine-learning based deep potential, we predict the pressure-
             induced fast diffusion of 1/4-occupied O atoms along the one-
             dimensional channel in Pb10(PO4)6O at 500 K, while the frameworks of
             Pb triangles and PO4 tetrahedrons remain stable. The calculation
             results also indicate Cu doping can provide appropriate effective
             chemical pressure, indicating the one-dimensional ion diffusion
             channel may appear in Pb9Cu(PO4)6O, even at ambient pressure. Our
             finding shows that the one-dimensional ions diffusion channel may be
             an important factor to affects the fabrication and electrical
             measurement of doped lead-apatite.},
}

@Article{Crippa_MachLearnSciTechnol_2023_v4_p45044,
    author =   {Martina Crippa and Annalisa Cardellini and Matteo Cioni and G{\'a}bor
             Cs{\'a}nyi and Giovanni M Pavan},
    title =    {{Machine learning of microscopic structure-dynamics relationships in
             complex molecular systems}},
    journal =  {Mach, Learn.,: Sci, Technol,},
    year =     2023,
    volume =   4,
    number =   4,
    pages =    45044,
    doi =      {10.1088/2632-2153/ad0fa5},
    abstract = {<jats:title>Abstract</jat
             s:title>
             <jats:p>In many complex molecular
             systems, the macroscopic ensemble{\textquoteright}s properties are
             controlled by microscopic dynamic events (or fluctuations) that are
             often difficult to detect via pattern-recognition approaches.
             Discovering the relationships between local structural environments
             and the dynamical events originating from them would allow unveiling
             microscopic-level structure-dynamics relationships fundamental to
             understand the macroscopic behavior of complex systems. Here we show
             that, by coupling advanced structural (e.g. Smooth Overlap of Atomic
             Positions, SOAP) with local dynamical descriptors (e.g. Local
             Environment and Neighbor Shuffling, LENS) in a unique dataset, it is
             possible to improve both individual SOAP- and LENS-based analyses,
             obtaining a more complete characterization of the system under study.
             As representative examples, we use various molecular systems with
             diverse internal structural dynamics. On the one hand, we demonstrate
             how the combination of structural and dynamical descriptors
             facilitates decoupling relevant dynamical fluctuations from noise,
             overcoming the intrinsic limits of the individual analyses.
             Furthermore, machine learning approaches also allow extracting from
             such combined structural/dynamical dataset useful microscopic-level
             relationships, relating key local dynamical events (e.g. LENS
             fluctuations) occurring in the systems to the local structural (SOAP)
             environments they originate from. Given its abstract nature, we
             believe that such an approach will be useful in revealing hidden
             microscopic structure-dynamics relationships fundamental to
             rationalize the behavior of a variety of complex systems, not
             necessarily limited to the atomistic and molecular
             scales.</jats:p>},
}

@Article{San_NatCommun_2023_v14_p7858,
    author =   {Xingyuan San and Junwei Hu and Mingyi Chen and Haiyang Niu and Paul J.
             M. Smeets and Christos D. Malliakas and Jie Deng and Kunmo Koo and
             Roberto {\{}Dos Reis{\}} and Vinayak P. Dravid and Xiaobing Hu},
    title =    {{Unlocking the mysterious polytypic features within vaterite CaCO3}},
    journal =  {Nat. Commun.},
    year =     2023,
    volume =   14,
    number =   1,
    pages =    7858,
    doi =      {10.1038/s41467-023-43625-0},
    abstract = {Calcium carbonate (CaCO3), the most abundant biogenic mineral on
             earth, plays a crucial role in various fields such as hydrosphere,
             biosphere, and climate regulation. Of the four polymorphs, calcite,
             aragonite, vaterite, and amorphous CaCO3, vaterite is the most
             enigmatic one due to an ongoing debate regarding its structure that
             has persisted for nearly a century. In this work, based on systematic
             transmission electron microscopy characterizations, crystallographic
             analysis and machine learning aided molecular dynamics simulations
             with ab initio accuracy, we reveal that vaterite can be regarded as a
             polytypic structure. The basic phase has a monoclinic lattice
             possessing pseudohexagonal symmetry. Direct imaging and atomic-scale
             simulations provide evidence that a single grain of vaterite can
             contain three orientation variants. Additionally, we find that
             vaterite undergoes a second-order phase transition with a critical
             point of {\textasciitilde}190{\,}K. These atomic scale insights
             provide a comprehensive understanding of the structure of vaterite and
             offer advanced perspectives on the biomineralization process of
             calcium carbonate.},
}

@Article{Zeng_NpjComputMater_2023_v9_p213,
    author =   {Qiyu Zeng and Bo Chen and Shen Zhang and Dongdong Kang and Han Wang
             and Xiaoxiang Yu and Jiayu Dai},
    title =    {{Full-scale ab initio simulations of laser-driven atomistic dynamics}},
    journal =  {Npj Comput. Mater},
    year =     2023,
    volume =   9,
    number =   1,
    pages =    213,
    doi =      {10.1038/s41524-023-01168-4},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>The
             coupling of excited states and ionic dynamics is the basic and
             challenging point for the materials response at extreme conditions. In
             the laboratory, the intense laser produces transient nature and
             complexity with highly nonequilibrium states, making it extremely
             difficult and interesting for both experimental measurements and
             theoretical methods. With the inclusion of laser-excited states, we
             extend an ab initio method into the direct simulations of whole laser-
             driven microscopic dynamics from solid to liquid. We construct the
             framework of combining the electron-temperature-dependent deep neural-
             network potential energy surface with a hybrid atomistic-continuum
             approach, controlling non-adiabatic energy exchange and atomistic
             dynamics, which enables consistent interpretation of experimental
             data. By large-scale ab initio simulations, we demonstrate that the
             nonthermal effects introduced by hot electrons play a dominant role in
             modulating the lattice dynamics, thermodynamic pathway, and structural
             transformation. We highlight that the present work provides a path to
             realistic computational studies of laser-driven processes, thus
             bridging the gap between experiments and
             simulations.</jats:p>},
}

@Article{deVilla_NatCommun_2023_v14_p7580,
    author =   {Kyla {\{}de Villa{\}} and Felipe Gonz{\'a}lez-Cataldo and Burkhard
             Militzer},
    title =    {{Double superionicity in icy compounds at planetary interior conditions}},
    journal =  {Nat. Commun.},
    year =     2023,
    volume =   14,
    number =   1,
    pages =    7580,
    doi =      {10.1038/s41467-023-42958-0},
    abstract = {The elements hydrogen, carbon, nitrogen and oxygen are assumed to
             comprise the bulk of the interiors of the ice giant planets Uranus,
             Neptune, and sub-Neptune exoplanets. The details of their interior
             structures have remained largely unknown because it is not understood
             how the compounds H2O, NH3 and CH4 behave and react once they have
             been accreted and exposed to high pressures and temperatures. Here we
             study thirteen H-C-N-O compounds with ab initio computer simulations
             and demonstrate that they assume a superionic state at elevated
             temperatures, in which the hydrogen ions diffuse through a stable
             sublattice that is provided by the larger nuclei. At yet higher
             temperatures, four of the thirteen compounds undergo a second
             transition to a novel doubly superionic state, in which the smallest
             of the heavy nuclei diffuse simultaneously with hydrogen ions through
             the remaining sublattice. Since this transition and the melting
             transition at yet higher temperatures are both of first order, this
             may introduce additional layers in the mantle of ice giant planets and
             alter their convective patterns.},
}

@Article{Guo_JChemPhys_2023_v159_p204702,
    author =   {Fangyu Guo and Bo Chen and Qiyu Zeng and Xiaoxiang Yu and Kaiguo Chen
             and Dongdong Kang and Yong Du and Jianhua Wu and Jiayu Dai},
    title =    {{Microstructure evolution under thermo-mechanical operating of
             rocksalt-structure TiN via neural network potential}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   159,
    number =   20,
    pages =    204702,
    doi =      {10.1063/5.0171528},
    abstract = {In the process of high temperature service, the mechanical properties
             of cutting tools decrease sharply due to the peeling of the protective
             coating. However, the mechanism of such coating failure remains
             obscure due to the complicated interaction between atomic structure,
             temperature, and stress. This dynamic evolution nature demands both
             large system sizes and accurate description on the atomic scale,
             raising challenges for existing atomic scale calculation methods.
             Here, we developed a deep neural network (DNN) potential for Ti-N
             binary systems based on first-principles study datasets to achieve
             quantum-accurate large-scale atomic simulation. Compared with
             empirical interatomic potential based on the embedded-atom-method, the
             developed DNN-potential can accurately predict lattice constants,
             phonon properties, and mechanical properties under various
             thermodynamic conditions. Moreover, for the first time, we present the
             atomic evolution of the fracture behavior of large-scale rocksalt-
             structure (B1) TiN systems coupled with temperature and stress
             conditions. Our study validates that interatomic brittle fractures
             occur when TiN stretches beyond its tensile yield point. Such
             simulation of coating fracture and cutting behavior based on large-
             scale atoms can shed new light on understanding the microstructure and
             mechanical properties of coating tools under extreme operating
             conditions.},
}

@Article{Lim_JPhysChemC_2023_v127_p22692,
    author =   {Jinyoung Lim and Youngseon Shim and Jaehong Park and Hongkee Yoon and
             Munbo Shim and Young-Gu Kim and Dae Sin Kim},
    title =    {{Molecular Dynamics Study of Silicon Carbide Using an Ab Initio-Based
             Neural Network Potential: Effect of Composition and Temperature on
             Crystallization Behavior}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    number =   46,
    pages =    {22692--22703},
    doi =      {10.1021/acs.jpcc.3c04224},
}

@Article{delaPuente_JAmChemSoc_2023_v145_p25186,
    author =   {Miguel {\{}de la Puente{\}} and Damien Laage},
    title =    {{How the Acidity of Water Droplets and Films Is Controlled by the Air-
             Water Interface}},
    journal =  {J. Am. Chem. Soc.},
    year =     2023,
    volume =   145,
    number =   46,
    pages =    {25186--25194},
    doi =      {10.1021/jacs.3c07506},
    abstract = {Acidity is a key determinant of chemical reactivity in atmospheric
             aqueous aerosols and water microdroplets used for catalysis. However,
             many fundamental questions about these systems have remained elusive,
             including how their acidity differs from that of bulk solutions, the
             degree of heterogeneity between their core and surface, and how the
             acid-base properties are affected by their size. Here, we perform
             hybrid density functional theory (DFT)-quality neural network-based
             molecular simulations with explicit nuclear quantum effects and
             combine them with an analytic model to describe the pH and self-ion
             concentrations of droplets and films for sizes ranging from nm to
             {\ensuremath{\mu}}m. We determine how the acidity of water droplets
             and thin films is controlled by the properties of the air-water
             interface and by their surface-to-volume ratio. We show that while the
             pH is uniform in each system, hydronium and hydroxide ions exhibit
             concentration gradients that span the two outermost molecular layers,
             enriching the interface with hydronium cations and depleting it with
             hydroxide anions. Acidity depends strongly on the surface-to-volume
             ratio for system sizes below a few tens of nanometers, where the core
             becomes enriched in hydroxide ions and the pH increases as a result of
             hydronium stabilization at the interface. These results obtained for
             pure water systems have important implications for our understanding
             of chemical reactivity in atmospheric aerosols and for catalysis in
             aqueous microdroplets.},
}

@Article{Xiao_JApplPhys_2023_v134,
    author =   {R. L. Xiao and K. L. Liu and Y. Ruan and L. Hu and B. Wei},
    title =    {{Tutorial: Deep learning prediction of thermophysical properties for
             liquid multicomponent alloys}},
    journal =  {J. Appl. Phys.},
    year =     2023,
    volume =   134,
    number =   19,
    doi =      {10.1063/5.0173250},
    abstract = {<jats:p>The thermophysical properties of
             liquid metals and alloys are crucial to explore the intrinsic
             mechanisms of the solidification process, glass formation, and fluid
             dynamics. The deep learning approaches have emerged as powerful tools
             in numerous scientific fields and exhibit extraordinary accuracy in
             the estimation of physical properties and structural characteristics
             for various materials. In this Tutorial, focusing on the
             thermophysical properties of liquid multicomponent alloys, deep
             learning methods, including both supervised learning and active
             learning, are introduced. Combined with the verification from
             electrostatic and electromagnetic levitation experiments, the
             influences of training parameters and methods on the accuracy to
             obtain interatomic potential by deep learning are revealed on the
             basis of deep neural network algorithm. As a result, this prediction
             method of liquid state properties for multicomponent alloys exhibited
             the dual advantages of high accuracy derived from density functional
             theory and low computational cost associated with empirical
             potential.</jats:p>},
}

@Article{Gromoff_Nanoscale_2023_v16_p384,
    author =   {Quentin Gromoff and Patrizio Benzo and Wissam A. Saidi and Christopher
             M. Andolina and Marie-Jos{\'e} Casanove and Teresa Hungria and Sophie
             Barre and Magali Benoit and Julien Lam},
    title =    {{Exploring the formation of gold/silver nanoalloys with gas-phase
             synthesis and machine-learning assisted simulations}},
    journal =  {Nanoscale},
    year =     2023,
    volume =   16,
    number =   1,
    pages =    {384--393},
    doi =      {10.1039/d3nr04471h},
    abstract = {While nanoalloys are of paramount scientific and practical interest,
             the main processes leading to their formation are still poorly
             understood. Key structural features in the alloy systems, including
             the crystal phase, chemical ordering, and morphology, are challenging
             to control at the nanoscale, making it difficult to extend their use
             to industrial applications. In this contribution, we focus on the
             gold/silver system that has two of the most prevalent noble metals and
             combine experiments with simulations to uncover the formation
             mechanisms at the atomic level. Nanoparticles were produced using a
             state-of-the-art inert-gas aggregation source and analyzed using
             transmission electron microscopy and energy-dispersive X-ray
             spectroscopy. Machine-learning-assisted molecular dynamics simulations
             were employed to model the crystallization process from liquid
             droplets to nanocrystals. Our study finds a preponderance of
             nanoparticles with five-fold symmetric morphology, including
             icosahedra and decahedra which is consistent with previous results on
             mono-metallic nanoparticles. However, we observed that gold atoms,
             rather than silver atoms, segregate at the surface of the obtained
             nanoparticles for all the considered alloy compositions. These
             segregation tendencies are in contrast to previous studies and have
             consequences on the crystallization dynamics and the subsequent
             crystal ordering. We finally showed that the underpinning of this
             surprising segregation dynamics is due to charge transfer and
             electrostatic interactions rather than surface energy considerations.},
}

@Article{Sun_Macromolecules_2023_v56_p9003,
    author =   {Yaguang Sun and Kaiwei Wan and Wenhui Shen and Jianxin He and Tong
             Zhou and Hui Wang and Hua Yang and Xinghua Shi},
    title =    {{Modeling Exchange Reactions in Covalent Adaptable Networks with
             Machine Learning Force Fields}},
    journal =  {Macromolecules},
    year =     2023,
    volume =   56,
    number =   21,
    pages =    {9003--9013},
    doi =      {10.1021/acs.macromol.3c01377},
}

@Article{Chen_JChemTheoryComput_2023_v19_p7861,
    author =   {Xiangyu Chen and William Shao and Nam Q. Le and Paulette Clancy},
    title =    {{Transferable Force Field for Gallium Nitride Crystal Growth from the
             Melt Using On-The-Fly Active Learning}},
    journal =  {J. Chem. Theory Comput.},
    year =     2023,
    volume =   19,
    number =   21,
    pages =    {7861--7872},
    doi =      {10.1021/acs.jctc.3c00587},
    abstract = {Atomic-scale simulations of reactive processes have been stymied by
             two factors: the lack of a suitable semiempirical force field on one
             hand and the impractically large computational burden of using ab
             initio molecular dynamics on the other hand. In this paper, we use an
             {''}on-the-fly{''} active learning technique to develop a
             nonparameterized force field that, in essence, exhibits the accuracy
             of density functional theory and the speed of a classical molecular
             dynamics simulation. We developed a force field capable of capturing
             the crystallization of gallium nitride (GaN) during a novel additive
             manufacturing process featuring the reaction of liquid Ga and gaseous
             nitrogen precursors to grow crystalline GaN thin films. We show that
             this machine learning model is capable of producing a single force
             field that can model solid, liquid, and gas phases involved in the
             process. We verified our computational predictions against a range of
             experimental measurements relevant to each phase and against ab initio
             calculations, showing that this nonparametric force field produces
             properties with excellent accuracy as well as exhibits computationally
             tractable efficiency. The force field is capable of allowing us to
             simulate the solid-liquid coexistence interface and the
             crystallization of GaN from the melt. The development of this
             transferable force field opens the opportunity to simulate the liquid-
             phase epitaxial growth more accurately than before to analyze reaction
             and diffusion processes and ultimately to establish a growth model of
             the additive manufacturing process to create the gallium nitride thin
             films.},
}

@Article{QiuQiu_ChinPhysLett_2023_v40_p116301,
    author =   {Rong Rong  {\{}Qiu Qiu {\}} and Qiyu Qi Yu  {\{}Zeng Ceng {\}} and Han
             Han  {\{}Wang Wang {\}} and Dongdong Dong Dong  {\{}Kang Kang {\}} and
             Xiaoxiang Xiao Xiang  {\{}Yu Yu {\}} and Jiayu Jia Yu  {\{}Dai Dai
             {\}}},
    title =    {{Anomalous Thermal Transport across the Superionic Transition in Ice}},
    journal =  {Chin. Phys, Lett,},
    year =     2023,
    volume =   40,
    number =   11,
    pages =    116301,
    doi =      {10.1088/0256-307X/40/11/116301},
    abstract = {<jats:p>Superionic ices with highly
             mobile protons within stable oxygen sub-lattices occupy an important
             proportion of the phase diagram of ice and widely exist in the
             interior of icy giants and throughout the Universe. Understanding the
             thermal transport in superionic ice is vital for the thermal evolution
             of icy planets. However, it is highly challenging due to the extreme
             thermodynamic conditions and dynamical nature of protons, beyond the
             capability of the traditional lattice dynamics and empirical potential
             molecular dynamics approaches. By utilizing the deep potential
             molecular dynamics approach, we investigate the thermal conductivity
             of ice-VII and superionic ice-VII{''} along the isobar of {\ensuremath
             {<}}jats:italic>P</jats:italic{\ensurema
             th{>}} = 30 GPa. A non-monotonic trend of thermal conductivity with
             elevated temperature is observed. Through heat flux decomposition and
             trajectory-based spectra analysis, we show that the thermally
             activated proton diffusion in ice-VII and superionic ice-VII{''}
             contribute significantly to heat convection, while the broadening in
             vibrational energy peaks and significant softening of transverse
             acoustic branches lead to a reduction in heat conduction. The
             competition between proton diffusion and phonon scattering results in
             anomalous thermal transport across the superionic transition in ice.
             This work unravels the important role of proton diffusion in the
             thermal transport of high-pressure ice. Our approach provides new
             insights into modeling the thermal transport and atomistic dynamics in
             superionic materials.</jats:p>},
}

@Article{Wu_PhysRevB_2023_v108_pL180104,
    author =   {Jing Wu and Jiyuan Yang and Yuan-Jinsheng Liu and Duo Zhang and Yudi
             Yang and Yuzhi Zhang and Linfeng Zhang and Shi Liu},
    title =    {{Universal interatomic potential for perovskite oxides}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   108,
    number =   18,
    pages =    {L180104},
    doi =      {10.1103/PhysRevB.108.L180104},
    abstract = {With their celebrated structural and chemical flexibility, perovskite
             oxides have served as a highly adaptable material platform for
             exploring emergent phenomena arising from the interplay between
             different degrees of freedom. Molecular dynamics (MD) simulations
             leveraging classical force fields, commonly depicted as parameterized
             analytical functions, have made significant contributions in
             elucidating the atomistic dynamics and structural properties of
             crystalline solids including perovskite oxides. However, the force
             fields currently available for solids are rather specific and offer
             limited transferability, making it time-consuming to use MD to study
             new materials systems since a new force field must be parameterized
             and tested first. The lack of a generalized force field applicable to
             a broad spectrum of solid materials hinders the facile deployment of
             MD in computer-aided materials discovery (CAMD). Here, by utilizing a
             deep-neural network with a self-attention scheme, we have developed a
             unified force field that enables MD simulations of perovskite oxides
             involving 14 metal elements and conceivably their solid solutions with
             arbitrary compositions. Notably, isobaric-isothermal ensemble MD
             simulations with this model potential accurately predict the
             experimental phase transition sequences for several markedly different
             ferroelectric oxides, including a 6-element ternary solid solution, Pb
             (In{\$}{\_}{\{}1/2{\}}{\$}Nb{\$}{\_}{\{}1/2{\}}{\$})O{\$}{\_}3{\$}--
             Pb(Mg{\$}{\_}{\{}1/3{\}}{\$}Nb{\$}{\_}{\{}2/3{\}}{\$})O{\$}{\_}3{\$}--
             PbTiO{\$}{\_}3{\$}. We believe the universal interatomic potential
             along with the training database, proposed regression tests, and the
             auto-testing workflow, all released publicly, will pave the way for a
             systematic improvement and extension of a unified force field for
             solids, potentially heralding a new era in CAMD.},
}

@Article{Lei_PhysRevB_2023_v108_p184105,
    author =   {Zhen-Shuai Lei and Xiao-Wei Sun and Xin-Xuan Wang and Zi-Jiang Liu and
             Ting Song and Jun-Hong Tian},
    title =    {{Influence of temperature on the process of hydrogen bond
             symmetrization in $\varepsilon$-
             FeOOH}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   108,
    number =   18,
    pages =    184105,
    doi =      {10.1103/PhysRevB.108.184105},
}

@Article{Bhatt_PhysRevMater_2023_v7_p115001,
    author =   {Niraj Bhatt and Pravin Karna and Sandip Thakur and Ashutosh Giri},
    title =    {{Transition from electron-dominated to phonon-driven thermal transport
             in tungsten under extreme pressures}},
    journal =  {Phys, Rev, Mater.},
    year =     2023,
    volume =   7,
    number =   11,
    pages =    115001,
    doi =      {10.1103/PhysRevMaterials.7.115001},
}

@Article{Gong_PhysRevB_2023_v108_p134112,
    author =   {Zhanpeng Gong and Jefferson Zhe Liu and Xiangdong Ding and Jun Sun and
             Junkai Deng},
    title =    {{Strain-aided room-temperature second-order ferroelectric phase
             transition in monolayer PbTe: Deep potential molecular dynamics
             simulations}},
    journal =  {Phys. Rev. B},
    year =     2023,
    volume =   108,
    number =   13,
    pages =    134112,
    doi =      {10.1103/PhysRevB.108.134112},
}

@Article{Zhang_AcsPhysChemAu_2024,
    author =   {Pengchao Zhang and Muye Feng and Xuefei Xu},
    title =    {{Double-Layer Distribution of Hydronium and Hydroxide Ions in the
             Air{\textendash}Water Interface}},
    journal =  {Acs Phys. Chem Au},
    year =     2024,
    doi =      {10.1021/acsphyschemau.3c00076},
    abstract = {,},
}

@Article{Bonati_ProcNatlAcadSciUSA_2023_v120_pe2313023120,
    author =   {Luigi Bonati and Daniela Polino and Cristina Pizzolitto and
             Pierdomenico Biasi and Rene Eckert and Stephan Reitmeier and Robert
             Schl{\"o}gl and Michele Parrinello},
    title =    {{The role of dynamics in heterogeneous catalysis: Surface diffusivity
             and N2 decomposition on Fe(111)}},
    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
    year =     2023,
    volume =   120,
    number =   50,
    pages =    {e2313023120},
    doi =      {10.1073/pnas.2313023120},
    abstract = {Dynamics has long been recognized to play an important role in
             heterogeneous catalytic processes. However, until recently, it has
             been impossible to study their dynamical behavior at industry-relevant
             temperatures. Using a combination of machine learning potentials and
             advanced simulation techniques, we investigate the cleavage of the
             N[Formula: see text] triple bond on the Fe(111) surface. We find that
             at low temperatures our results agree with the well-established
             picture. However, if we increase the temperature to reach operando
             conditions, the surface undergoes a global dynamical change and the
             step structure of the Fe(111) surface is destabilized. The catalytic
             sites, traditionally associated with this surface, appear and
             disappear continuously. Our simulations illuminate the danger of
             extrapolating low-temperature results to operando conditions and
             indicate that the catalytic activity can only be inferred from
             calculations that take dynamics fully into account. More than that,
             they show that it is the transition to this highly fluctuating
             interfacial environment that drives the catalytic process.},
}

@Article{Liu_PhysRevE_2023_v108_p55310,
    author =   {K. L. Liu and R. L. Xiao and Y. Ruan and B. Wei},
    title =    {{Active learning prediction and experimental confirmation of atomic
             structure and thermophysical properties for liquid
             Hf{\_}{\{}76{\}}W{\_}{\{}24{\}} refractory alloy}},
    journal =  {Phys. Rev., E},
    year =     2023,
    volume =   108,
    number =   {5-2},
    pages =    55310,
    doi =      {10.1103/PhysRevE.108.055310},
    abstract = {The determination of liquid atomic structure and thermophysical
             properties is essential for investigating the physical characteristics
             and phase transitions of refractory alloys. However, due to the
             stringent experimental requirements and underdeveloped interatomic
             potentials, acquiring such information through experimentation or
             simulation remains challenging. Here, an active learning method
             incorporating a deep neural network was established to generate the
             interatomic potential of the Hf{\_}{\{}76{\}}W{\_}{\{}24{\}}
             refractory alloy. Then the achieved potential was applied to
             investigate the liquid atomic structure and thermophysical properties
             of this alloy over a wide temperature range. The simulation results
             revealed the distinctive bonding preferences among atoms, that is, Hf
             atoms exhibited a strong tendency for conspecific bonding, while W
             atoms preferred to form an interspecific bonding. The analysis of
             short-range order (SRO) in the liquid alloy revealed a significant
             proportion of icosahedral (ICO) and distorted ICO structures, which
             even exceeded 30{\%} in the undercooled state. As temperature
             decreased, SRO structures demonstrated an increase in larger
             coordination number (CN) clusters and a decrease in smaller CNs. The
             alterations of the atomic structure indicated that the liquid alloy
             becomes more ordered, densely packed, and energetically favorable with
             decreasing temperature, consistent with the obtained fact: Both
             density and surface tension increase linearly. The simulated
             thermophysical properties were close to experimental values with minor
             deviations of 2.8{\%} for density and 3.4{\%} for surface tension. The
             consistency of the thermophysical properties further attested to the
             accuracy and reliability of active learning simulation.},
}

@Article{Zhang_NatCommun_2024_v15_p4223,
    author =   {Xiao-Wei Zhang and Chong Wang and Xiaoyu Liu and Yueyao Fan and Ting
             Cao and Di Xiao},
    title =    {{Polarization-driven band topology evolution in twisted MoTe2 and WSe2}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    4223,
    doi =      {10.1038/s41467-024-48511-x},
    abstract = {Motivated by recent experimental observations of opposite Chern
             numbers in R-type twisted MoTe2 and WSe2 homobilayers, we perform
             large-scale density-functional-theory calculations with machine
             learning force fields to investigate moir{\'e} band topology across a
             range of twist angles in both materials. We find that the Chern
             numbers of the moir{\'e} frontier bands change sign as a function of
             twist angle, and this change is driven by the competition between
             moir{\'e} ferroelectricity and piezoelectricity. Our large-scale
             calculations, enabled by machine learning methods, reveal crucial
             insights into interactions across different scales in twisted bilayer
             systems. The interplay between atomic-level relaxation effects and
             moir{\'e}-scale electrostatic potential variation opens new avenues
             for the design of intertwined topological and correlated states,
             including the possibility of mimicking higher Landau level physics in
             the absence of magnetic field.},
}

@Article{Hedman_NatCommun_2024_v15_p4076,
    author =   {Daniel Hedman and Ben McLean and Christophe Bichara and Shigeo
             Maruyama and J. Andreas Larsson and Feng Ding},
    title =    {{Dynamics of growing carbon nanotube interfaces probed by machine
             learning-enabled molecular simulations}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    4076,
    doi =      {10.1038/s41467-024-47999-7},
    abstract = {Carbon nanotubes (CNTs), hollow cylinders of carbon, hold great
             promise for advanced technologies, provided their structure remains
             uniform throughout their length. Their growth takes place at high
             temperatures across a tube-catalyst interface. Structural defects
             formed during growth alter CNT properties. These defects are believed
             to form and heal at the tube-catalyst interface but an understanding
             of these mechanisms at the atomic-level is lacking. Here we present
             DeepCNT-22, a machine learning force field (MLFF) to drive molecular
             dynamics simulations through which we unveil the mechanisms of CNT
             formation, from nucleation to growth including defect formation and
             healing. We find the tube-catalyst interface to be highly dynamic,
             with large fluctuations in the chiral structure of the CNT-edge. This
             does not support continuous spiral growth as a general mechanism,
             instead, at these growth conditions, the growing tube edge exhibits
             significant configurational entropy. We demonstrate that defects form
             stochastically at the tube-catalyst interface, but under low growth
             rates and high temperatures, these heal before becoming incorporated
             in the tube wall, allowing CNTs to grow defect-free to seemingly
             unlimited lengths. These insights, not readily available through
             experiments, demonstrate the remarkable power of MLFF-driven
             simulations and fill long-standing gaps in our understanding of CNT
             growth mechanisms.},
}

@Article{Owen_NpjComputMater_2024_v10_p92,
    author =   {Cameron J. Owen and Steven B. Torrisi and Yu Xie and Simon Batzner and
             Kyle Bystrom and Jennifer Coulter and Albert Musaelian and Lixin Sun
             and Boris Kozinsky},
    title =    {{Complexity of many-body interactions in transition metals via machine-
             learned force fields from the TM23 data set}},
    journal =  {Npj Comput. Mater},
    year =     2024,
    volume =   10,
    number =   1,
    pages =    92,
    doi =      {10.1038/s41524-024-01264-z},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>This work
             examines challenges associated with the accuracy of machine-learned
             force fields (MLFFs) for bulk solid and liquid phases of {\ensuremath{
             <}}jats:italic>d</jats:italic{\ensuremat
             h{>}}-block elements. In exhaustive detail, we contrast the
             performance of force, energy, and stress predictions across the
             transition metals for two leading MLFF models: a kernel-based atomic
             cluster expansion method implemented using sparse Gaussian processes
             (FLARE), and an equivariant message-passing neural network (NequIP).
             Early transition metals present higher relative errors and are more
             difficult to learn relative to late platinum- and coinage-group
             elements, and this trend persists across model architectures. Trends
             in complexity of interatomic interactions for different metals are
             revealed via comparison of the performance of representations with
             different many-body order and angular resolution. Using arguments
             based on perturbation theory on the occupied and unoccupied {\ensurema
             th{<}}jats:italic>d</jats:italic{\ensure
             math{>}} states near the Fermi level, we determine that the large,
             sharp <jats:italic>d</jat
             s:italic> density of states both above and below the
             Fermi level in early transition metals leads to a more complex,
             harder-to-learn potential energy surface for these metals. Increasing
             the fictitious electronic temperature (smearing) modifies the angular
             sensitivity of forces and makes the early transition metal forces
             easier to learn. This work illustrates challenges in capturing
             intricate properties of metallic bonding with current leading MLFFs
             and provides a reference data set for transition metals, aimed at
             benchmarking the accuracy and improving the development of emerging
             machine-learned approximations.</jats:p>},
}

@Article{Fan_NatCommun_2024_v15_p3251,
    author =   {Dong Fan and Supriyo Naskar and Guillaume Maurin},
    title =    {{Unconventional mechanical and thermal behaviours of MOF CALF-20}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    3251,
    doi =      {10.1038/s41467-024-47695-6},
    abstract = {CALF-20 was recently identified as a benchmark sorbent for CO2 capture
             at the industrial scale, however comprehensive atomistic insight into
             its mechanical/thermal properties under working conditions is still
             lacking. In this study, we developed a general-purpose machine-learned
             potential (MLP) for the CALF-20 MOF framework that predicts the
             thermodynamic and mechanical properties of the structure at finite
             temperatures within first-principles accuracy. Interestingly, CALF-20
             was demonstrated to exhibit both negative area compression and
             negative thermal expansion. Most strikingly, upon application of the
             tensile strain along the [001] direction, CALF-20 was shown to display
             a distinct two-step elastic deformation behaviour, unlike typical MOFs
             that undergo plastic deformation after elasticity. Furthermore, this
             MOF was shown to exhibit a fracture strain of up to 27{\%} along the
             [001] direction at room temperature comparable to that of MOF glasses.
             These abnormal thermal and mechanical properties make CALF-20 as
             attractive material for flexible and stretchable electronics and
             sensors.},
}

@Article{Li_NatCommun_2024_v15_p2653,
    author =   {Tao Li and Yongyi Wu and Guoliang Yu and Shengxian Li and Yifeng Ren
             and Yadong Liu and Jiarui Liu and Hao Feng and Yu Deng and Mingxing
             Chen and Zhenyu Zhang and Tai Min},
    title =    {{Realization of sextuple polarization states and interstate switching
             in antiferroelectric CuInP2S6}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    2653,
    doi =      {10.1038/s41467-024-46891-8},
    abstract = {Realization of higher-order multistates with mutual interstate
             switching in ferroelectric materials is a perpetual drive for high-
             density storage devices and beyond-Moore technologies. Here we
             demonstrate experimentally that antiferroelectric van der Waals
             CuInP2S6 films can be controllably stabilized into double, quadruple,
             and sextuple polarization states, and a system harboring polarization
             order of six is also reversibly tunable into order of four or two.
             Furthermore, for a given polarization order, mutual interstate
             switching can be achieved via moderate electric field modulation.
             First-principles studies of CuInP2S6 multilayers help to reveal that
             the double, quadruple, and sextuple states are attributable to the
             existence of respective single, double, and triple ferroelectric
             domains with antiferroelectric interdomain coupling and Cu ion
             migration. These findings offer appealing platforms for developing
             multistate ferroelectric devices, while the underlining mechanism is
             transformative to other non-volatile material systems.},
}

@Article{AbouElKheir_NpjComputMater_2024_v10_p33,
    author =   {Omar {\{}Abou El Kheir{\}} and Luigi Bonati and Michele Parrinello and
             Marco Bernasconi},
    title =    {{Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change
             compound with a machine-learned interatomic potential}},
    journal =  {Npj Comput. Mater},
    year =     2024,
    volume =   10,
    number =   1,
    pages =    33,
    doi =      {10.1038/s41524-024-01217-6},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>The phase
             change compound Ge<jats:sub>2{\ensuremat
             h{<}}/jats:sub>Sb<jats:sub{\ensuremath{>
             }}2</jats:sub>Te<jats:sub
             >5</jats:sub> (GST225) is
             exploited in advanced non-volatile electronic memories and in
             neuromorphic devices which both rely on a fast and reversible
             transition between the crystalline and amorphous phases induced by
             Joule heating. The crystallization kinetics of GST225 is a key
             functional feature for the operation of these devices. We report here
             on the development of a machine-learned interatomic potential for
             GST225 that allowed us to perform large scale molecular dynamics
             simulations (over 10,000 atoms for over 100{\,}ns) to uncover the
             details of the crystallization kinetics in a wide range of
             temperatures of interest for the programming of the devices. The
             potential is obtained by fitting with a deep neural network (NN)
             scheme a large quantum-mechanical database generated within density
             functional theory. The availability of a highly efficient and yet
             highly accurate NN potential opens the possibility to simulate phase
             change materials at the length and time scales of the real
             devices.</jats:p>},
}

@Article{You_NpjComputMater_2024_v10_p57,
    author =   {Yiwei You and Dexin Zhang and Fulun Wu and Xinrui Cao and Yang Sun and
             Zi-Zhong Zhu and Shunqing Wu},
    title =    {{Principal component analysis enables the design of deep learning
             potential precisely capturing LLZO phase transitions}},
    journal =  {Npj Comput. Mater},
    year =     2024,
    volume =   10,
    number =   1,
    pages =    57,
    doi =      {10.1038/s41524-024-01240-7},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>The
             development of accurate and efficient interatomic potentials using
             machine learning has emerged as an important approach in materials
             simulations and discovery. However, the systematic construction of
             diverse, converged training sets remains challenging. We develop a
             deep learning-based interatomic potential for the Li<ja
             ts:sub>7</jats:sub>La{\en
             suremath{<}}jats:sub>3</jats:sub{\ensure
             math{>}}Zr<jats:sub>2</ja
             ts:sub>O<jats:sub>12{\ens
             uremath{<}}/jats:sub> (LLZO) system. Our interatomic
             potential is trained using a diverse dataset obtained from databases
             and first-principles simulations. We propose using the coverage of the
             training and test sets as the convergence criteria for the training
             iterations, where the coverage is calculated by principal component
             analysis. This results in an accurate LLZO interatomic potential that
             can describe the structure and dynamical properties of LLZO systems
             meanwhile greatly reducing computational costs compared to density
             functional theory calculations. The interatomic potential accurately
             describes radial distribution functions and thermal expansion
             coefficient consistent with experiments. It also predicts the
             tetragonal-to-cubic phase transition behaviors of LLZO systems. Our
             work provides an efficient training strategy to develop accurate deep-
             learning interatomic potential for complex solid-state electrolyte
             materials, providing a promising simulation tool to accelerate solid-
             state battery design and
             applications.</jats:p>},
}

@Article{Qi_NpjComputMater_2024_v10_p43,
    author =   {Ji Qi and Tsz Wai Ko and Brandon C. Wood and Tuan Anh Pham and Shyue
             Ping Ong},
    title =    {{Robust training of machine learning interatomic potentials with
             dimensionality reduction and stratified sampling}},
    journal =  {Npj Comput. Mater},
    year =     2024,
    volume =   10,
    number =   1,
    pages =    43,
    doi =      {10.1038/s41524-024-01227-4},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>Machine
             learning interatomic potentials (MLIPs) enable accurate simulations of
             materials at scales beyond that accessible by ab initio methods and
             play an increasingly important role in the study and design of
             materials. However, MLIPs are only as accurate and robust as the data
             on which they are trained. Here, we present DImensionality-Reduced
             Encoded Clusters with sTratified (DIRECT) sampling as an approach to
             select a robust training set of structures from a large and complex
             configuration space. By applying DIRECT sampling on the Materials
             Project relaxation trajectories dataset with over one million
             structures and 89 elements, we develop an improved materials 3-body
             graph network (M3GNet) universal potential that extrapolates more
             reliably to unseen structures. We further show that molecular dynamics
             (MD) simulations with the M3GNet universal potential can be used
             instead of expensive ab initio MD to rapidly create a large
             configuration space for target systems. We combined this scheme with
             DIRECT sampling to develop a reliable moment tensor potential for
             titanium hydrides without the need for iterative augmentation of
             training structures. This work paves the way for robust high-
             throughput development of MLIPs across any compositional
             complexity.</jats:p>},
}

@Article{Wieser_NpjComputMater_2024_v10_p18,
    author =   {Sandro Wieser and Egbert Zojer},
    title =    {{Machine learned force-fields for an Ab-initio quality description of
             metal-organic frameworks}},
    journal =  {Npj Comput. Mater},
    year =     2024,
    volume =   10,
    number =   1,
    pages =    18,
    doi =      {10.1038/s41524-024-01205-w},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>Metal-
             organic frameworks (MOFs) are an incredibly diverse group of highly
             porous hybrid materials, which are interesting for a wide range of
             possible applications. For a meaningful theoretical description of
             many of their properties accurate and computationally highly efficient
             methods are in high demand. These would avoid compromises regarding
             either the quality of modelling results or the level of complexity of
             the calculated properties. With the advent of machine learning
             approaches, it is now possible to generate such approaches with
             relatively little human effort. Here, we build on existing types of
             machine-learned force fields belonging to the moment-tensor and
             kernel-based potential families to develop a recipe for their
             efficient parametrization. This yields exceptionally accurate and
             computationally highly efficient force fields. The parametrization
             relies on reference configurations generated during molecular dynamics
             based, active learning runs. The performance of the potentials is
             benchmarked for a representative selection of commonly studied MOFs
             revealing a close to DFT accuracy in predicting forces and structural
             parameters for a set of validation structures. The same applies to
             elastic constants and phonon band structures. Additionally, for MOF-5
             the thermal conductivity is obtained with full quantitative agreement
             to single-crystal experiments. All this is possible while maintaining
             a very high degree of computational efficiency. The exceptional
             accuracy of the parameterized force field potentials combined with
             their computational efficiency has the potential of lifting the
             computational modelling of MOFs to the next
             level.</jats:p>},
}

@Article{DeAngelis_SciRep_2024_v14_p978,
    author =   {Paolo {\{}De Angelis{\}} and Roberta Cappabianca and Matteo Fasano and
             Pietro Asinari and Eliodoro Chiavazzo},
    title =    {{Enhancing ReaxFF for molecular dynamics simulations of lithium-ion
             batteries: an interactive reparameterization protocol}},
    journal =  {Sci. Rep.},
    year =     2024,
    volume =   14,
    number =   1,
    pages =    978,
    doi =      {10.1038/s41598-023-50978-5},
    abstract = {Lithium-ion batteries (LIBs) have become an essential technology for
             the green economy transition, as they are widely used in portable
             electronics, electric vehicles, and renewable energy systems. The
             solid-electrolyte interphase (SEI) is a key component for the correct
             operation, performance, and safety of LIBs. The SEI arises from the
             initial thermal metastability of the anode-electrolyte interface, and
             the resulting electrolyte reduction products stabilize the interface
             by forming an electrochemical buffer window. This article aims to make
             a first-but important-step towards enhancing the parametrization of a
             widely-used reactive force field (ReaxFF) for accurate molecular
             dynamics (MD) simulations of SEI components in LIBs. To this end, we
             focus on Lithium Fluoride (LiF), an inorganic salt of great interest
             due to its beneficial properties in the passivation layer. The
             protocol relies heavily on various Python libraries designed to work
             with atomistic simulations allowing robust automation of all the
             reparameterization steps. The proposed set of configurations, and the
             resulting dataset, allow the new ReaxFF to recover the solid nature of
             the inorganic salt and improve the mass transport properties
             prediction from MD simulation. The optimized ReaxFF surpasses the
             previously available force field by accurately adjusting the
             diffusivity of lithium in the solid lattice, resulting in a two-order-
             of-magnitude improvement in its prediction at room temperature.
             However, our comprehensive investigation of the simulation shows the
             strong sensitivity of the ReaxFF to the training set, making its
             ability to interpolate the potential energy surface challenging.
             Consequently, the current formulation of ReaxFF can be effectively
             employed to model specific and well-defined phenomena by utilizing the
             proposed interactive reparameterization protocol to construct the
             dataset. Overall, this work represents a significant initial step
             towards refining ReaxFF for precise reactive MD simulations, shedding
             light on the challenges and limitations of ReaxFF force field
             parametrization. The demonstrated limitations emphasize the potential
             for developing more versatile and advanced force fields to upscale ab
             initio simulation through our interactive reparameterization protocol,
             enabling more accurate and comprehensive MD simulations in the future.},
}

@Article{Zhang_JColloidInterfaceSci_2024_v671_p258,
    author =   {Meng Zhang and Yang Liu and Yun Duan and Xu Liu and Yan-Qin Wang},
    title =    {{Ce-doped copper oxide and copper vanadate Cu3VO4 hybrid for boosting
             nitrate electroreduction to ammonia}},
    journal =  {J. Colloid Interface Sci.},
    year =     2024,
    volume =   671,
    pages =    {258--269},
    doi =      {10.1016/j.jcis.2024.05.189},
    abstract = {The electrocatalytic nitrate reduction to ammonia reaction (ENO3RR)
             holds great potential as a cost-effective method for synthesizing
             ammonia. This work designed a cerium (Ce) doped Cu2+1O/Cu3VO4
             catalyst. The coupling of vanadium-based oxides with Cu2+1O
             effectively adjusts the catalyst's electronic structure, addressing
             the inherent issues of limited activity and low conductivity in
             typical copper-based oxides; moreover, Ce doping generates oxygen
             vacancies (Ov), providing more active sites and thereby enhancing the
             ENO3RR performance. The catalyst exhibits superior NH3Faradaic
             efficiency (93.7{~}{\%}) with a NH3 yield of 18.905{~}mg{~}h-1 cm-2at
             -0.5{~}V vs. RHE under alkaline conditions. This study provides
             guidance for the design of highly efficient catalysts for ENO3RR.},
}

@Article{Wang_ComputMaterSci_2024_v244_p113154,
    author =   {Xinwei Wang and Zi-Jiang Liu and Jin-Shan Feng and Meng-Ru Chen and
             Liang Li and Xiao-Wei Sun and Fubo Tian},
    title =    {{Construction and application of deep learning potential for CaO under
             high pressure}},
    journal =  {Comput. Mater. Sci.},
    year =     2024,
    volume =   244,
    pages =    113154,
    doi =      {10.1016/j.commatsci.2024.113154},
}

@Article{Bhatt_IntJHeatMassTransf_2024_v229_p125673,
    author =   {Niraj Bhatt and Pravin Karna and Sandip Thakur and Ashutosh Giri},
    title =    {{Pressure-driven enhancement of phonon contribution to the thermal
             conductivity of Iridium}},
    journal =  {Int. J. Heat Mass Transf.},
    year =     2024,
    volume =   229,
    pages =    125673,
    doi =      {10.1016/j.ijheatmasstransfer.2024.125673},
}

@Article{Wang_NanoEnergy_2024_v127_p109762,
    author =   {Jing Wang and Xinrong Yan and Xin Wang and Mingli Yang and Dingguo Xu},
    title =    {{Selective activation of methane on hydroxyapatite surfaces: Insights
             from machine learning and density functional theory}},
    journal =  {Nano Energy},
    year =     2024,
    volume =   127,
    pages =    109762,
    doi =      {10.1016/j.nanoen.2024.109762},
}

@Article{Feng_SolEnergyMaterSolCells_2024_v272_p112903,
    author =   {Taixi Feng and Jia Zhao and Guimin Lu},
    title =    {{Machine learning model to efficiently predict the structure and
             properties of MgCl2{\textendash}NaCl{\textendash}KCl melts}},
    journal =  {Sol. Energy Mater. Sol. Cells},
    year =     2024,
    volume =   272,
    pages =    112903,
    doi =      {10.1016/j.solmat.2024.112903},
}

@Article{Galvani_JPhysMater_2024_v7_p35003,
    author =   {Thomas Galvani and Ali K Hamze and Laura Caputo and Onurcan Kaya and
             Simon M-M Dubois and Luigi Colombo and Viet-Hung Nguyen and Yongwoo
             Shin and Hyeon-Jin Shin and Jean-Christophe Charlier and Stephan Roche},
    title =    {{Exploring dielectric properties in atomistic models of amorphous boron
             nitride}},
    journal =  {J. Phys. Mater.},
    year =     2024,
    volume =   7,
    number =   3,
    pages =    35003,
    doi =      {10.1088/2515-7639/ad4c06},
    abstract = {<jats:title>Abstract</jat
             s:title>
             <jats:p>We report a theoretical study of
             dielectric properties of models of amorphous Boron Nitride, using
             interatomic potentials generated by machine learning. We first perform
             first-principles simulations on small (about 100 atoms in the periodic
             cell) sample sizes to explore the emergence of mid-gap states and its
             correlation with structural features. Next, by using a simplified
             tight-binding electronic model, we analyse the dielectric functions
             for complex three dimensional models (containing about 10.000 atoms)
             embedding varying concentrations of sp<jats:sup{\ensure
             math{>}}1</jats:sup>, sp<
             jats:sup>2</jats:sup> and
             sp<jats:sup>3</jats:sup{\
             ensuremath{>}} bonds between B and N atoms. Within the limits of these
             methodologies, the resulting value of the zero-frequency dielectric
             constant is shown to be influenced by the population density of such
             mid-gap states and their localization characteristics. We observe
             nontrivial correlations between the structure-induced electronic
             fluctuations and the resulting dielectric constant values. Our
             findings are however just a first step in the quest of accessing fully
             accurate dielectric properties of as-grown amorphous BN of relevance
             for interconnect technologies and
             beyond.</jats:p>},
}

@Article{Che_CeramInt_2024_v50_p22865,
    author =   {Junwei Che and Wenjie Huang and Guoliang Ren and Jiajun Linghu and
             Xuezhi Wang},
    title =    {{Dual-channel phonon transport leads to low thermal conductivity in
             pyrochlore La2Hf2O7}},
    journal =  {Ceram. Int.},
    year =     2024,
    volume =   50,
    number =   13,
    pages =    {22865--22873},
    doi =      {10.1016/j.ceramint.2024.04.011},
}

@Article{Zhang_JMaterSciTechnol_2024_v185_p23,
    author =   {Linshuang Zhang and Manyi Yang and Shiwei Zhang and Haiyang Niu},
    title =    {{Unveiling the crystallization mechanism of cadmium selenide via
             molecular dynamics simulation with machine-learning-based deep
             potential}},
    journal =  {J. Mater. Sci. Technol.},
    year =     2024,
    volume =   185,
    pages =    {23--31},
    doi =      {10.1016/j.jmst.2023.09.059},
}

@Article{Wang_ComposBEng_2024_v279_p111452,
    author =   {Kai Wang and Guoqing Yao and Mengwei Lv and Zumin Wang and Yuan Huang
             and Wei Xi},
    title =    {{The nucleation and growth mechanism of solid-state amorphization and
             diffusion behavior at the W{\textendash}Cu interface}},
    journal =  {Compos. B: Eng.},
    year =     2024,
    volume =   279,
    pages =    111452,
    doi =      {10.1016/j.compositesb.2024.111452},
}

@Article{Hua_EnergyStorageMater_2024_v70_p103470,
    author =   {Haiming Hua and Fei Wang and Feng Wang and Jiayue Wu and Yaoqi Xu and
             Yichao Zhuang and Jing Zeng and Jinbao Zhao},
    title =    {{Machine learning molecular dynamics insight into high interface
             stability and fast kinetics of low-cost magnesium chloride amine
             electrolyte for rechargeable magnesium batteries}},
    journal =  {Energy Storage Mater.},
    year =     2024,
    volume =   70,
    pages =    103470,
    doi =      {10.1016/j.ensm.2024.103470},
}

@Article{Chen_WaterRes_2024_v256_p121580,
    author =   {Kai Chen and Chuling Guo and Chaoping Wang and Shoushi Zhao and Beiyi
             Xiong and Guining Lu and John R. Reinfelder and Zhi Dang},
    title =    {{Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid
             surface complexation-machine learning model}},
    journal =  {Water Res.},
    year =     2024,
    volume =   256,
    pages =    121580,
    doi =      {10.1016/j.watres.2024.121580},
    abstract = {This study aimed to develop surface complexation modeling-machine
             learning (SCM-ML) hybrid model for chromate and arsenate adsorption on
             goethite. The feasibility of two SCM-ML hybrid modeling approaches was
             investigated. Firstly, we attempted to utilize ML algorithms and
             establish the parameter model, to link factors influencing the
             adsorption amount of oxyanions with optimized surface complexation
             constants. However, the results revealed the optimized chromate or
             arsenate surface complexation constants might fall into local extrema,
             making it unable to establish a reasonable mapping relationship
             between adsorption conditions and surface complexation constants by ML
             algorithms. In contrast, species-informed models were successfully
             obtained, by incorporating the surface species information calculated
             from the unoptimized SCM with the adsorption condition as input
             features. Compared with the optimized SCM, the species-informed model
             could make more accurate predictions on pH edges, isotherms, and
             kinetic data for various input conditions (for chromate: root mean
             square error (RMSE) on test set = 5.90 {\%}; for arsenate: RMSE on
             test set = 4.84 {\%}). Furthermore, the utilization of the
             interpretable formula based on Local Interpretable Model-Agnostic
             Explanations (LIME) enabled the species-informed model to provide
             surface species information like SCM. The species-informed SCM-ML
             hybrid modeling method proposed in this study has great practicality
             and application potential, and is expected to become a new paradigm in
             surface adsorption model.},
}

@Article{Cao_IntJHeatMassTransf_2024_v224_p125359,
    author =   {Chenyang Cao and Shuo Cao and YuanXu Zhu and Haikuan Dong and Yanzhou
             Wang and Ping Qian},
    title =    {{Thermal transports of 2D phosphorous carbides by machine learning
             molecular dynamics simulations}},
    journal =  {Int. J. Heat Mass Transf.},
    year =     2024,
    volume =   224,
    pages =    125359,
    doi =      {10.1016/j.ijheatmasstransfer.2024.125359},
}

@Article{Shi_JAmCeramSoc_2024_v107_p3845,
    author =   {Zuhao Shi and Bin Liu and Yuanzheng Yue and Arramel Arramel and Neng
             Li},
    title =    {{Unraveling medium{-}range order and melting mechanism of ZIF{-}4 under
             high temperature}},
    journal =  {J Am Ceram Soc.},
    year =     2024,
    volume =   107,
    number =   6,
    pages =    {3845--3856},
    doi =      {10.1111/jace.19741},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>Glass
             formation in zeolitic imidazolate frameworks (ZIFs) has garnered
             significant attention in the field of metal{\textendash}organic
             frameworks (MOFs) in recent years. Numerous works have been conducted
             to investigate the microscopic mechanisms involved in the
             melting{\textendash}quenching process of ZIFs. Understanding the
             density variations that occur during the melting process of ZIFs is
             crucial for comprehending the origins of glass formation. However,
             conducting large{-}scale simulations has been challenging due to
             limitations in computational resources. In this work, we used
             deep{-}learning methods to accurately construct a potential function
             that describes the atomic{-}scale melting behavior of ZIF{-}4. The
             results revealed the spatial heterogeneity associated with the
             formation of low{-}density phases during the melting process of
             ZIF{-}4. This work discusses the advantages and limitations of
             applying deep{-}learning simulation methods to complex structures like
             ZIFs, providing valuable insights for the development of
             machine{-}learning approaches in designing MOF
             glasses.</jats:p>},
}

@Article{Zhang_JEurCeramSoc_2024_v44_p4243,
    author =   {Jin-Yu Zhang and Ga{\"e}l Huynh and Fu-Zhi Dai and Tristan Albaret and
             Shi-Hao Zhang and Shigenobu Ogata and David Rodney},
    title =    {{A deep-neural network potential to study transformation-induced
             plasticity in zirconia}},
    journal =  {J. Eur. Ceram. Soc.},
    year =     2024,
    volume =   44,
    number =   6,
    pages =    {4243--4254},
    doi =      {10.1016/j.jeurceramsoc.2024.01.007},
}

@Article{Sowa_JPhysChemC_2024_v128_p8724,
    author =   {Jakub K. Sowa and Danielle M. Cadena and Arshad Mehmood and Benjamin
             G. Levine and Sean T. Roberts and Peter J. Rossky},
    title =    {{IR Spectroscopy of Carboxylate-Passivated Semiconducting Nanocrystals:
             Simulation and Experiment}},
    journal =  {J. Phys. Chem. C},
    year =     2024,
    volume =   128,
    number =   21,
    pages =    {8724--8731},
    doi =      {10.1021/acs.jpcc.4c01988},
    abstract = {Surfaces of colloidal nanocrystals are frequently passivated with
             carboxylate ligands which exert significant effects on their
             optoelectronic properties and chemical stability. Experimentally,
             binding geometries of such ligands are typically investigated using
             vibrational spectroscopy, but the interpretation of the IR signal is
             usually not trivial. Here, using machine-learning (ML) algorithms
             trained on DFT data, we simulate an IR spectrum of a lead-rich PbS
             nanocrystal passivated with butyrate ligands. We obtain a good
             agreement with the experimental signal and demonstrate that the
             observed line shape stems from a very wide range of `tilted-
             bridge'-type geometries and does not indicate a coexistence of
             `bridging' and `chelating' binding modes as has been previously
             assumed. This work illustrates limitations of empirical spectrum
             assignment and demonstrates the effectiveness of ML-driven molecular
             dynamics simulations in reproducing IR spectra of nanoscopic systems.},
}

@Article{Wang_JAmChemSoc_2024_v146_p14566,
    author =   {Feng Wang and Zebing Ma and Jun Cheng},
    title =    {{Accelerating Computation of Acidity Constants and Redox Potentials for
             Aqueous Organic Redox Flow Batteries by Machine Learning Potential-
             Based Molecular Dynamics}},
    journal =  {J. Am. Chem. Soc.},
    year =     2024,
    volume =   146,
    number =   21,
    pages =    {14566--14575},
    doi =      {10.1021/jacs.4c01221},
    abstract = {Due to the increased concern about energy and environmental issues,
             significant attention has been paid to the development of large-scale
             energy storage devices to facilitate the utilization of clean energy
             sources. The redox flow battery (RFB) is one of the most promising
             systems. Recently, the high cost of transition-metal complex-based RFB
             has promoted the development of aqueous RFBs with redox-active organic
             molecules. To expand the working voltage, computational chemistry has
             been applied to search for organic molecules with lower or higher
             redox potentials. However, redox potential computation based on
             implicit solvation models would be challenging due to difficulty in
             parametrization when considering the complex solvation of supporting
             electrolytes. Besides, although ab initio molecular dynamics (AIMD)
             describes the supporting electrolytes with the same level of
             electronic structure theory as the redox couple, the application is
             impeded by the high computation costs. Recently, machine learning
             molecular dynamics (MLMD) has been illustrated to accelerate AIMD by
             several orders of magnitude without sacrificing the accuracy. It has
             been established that redox potentials can be computed by MLMD with
             two separated machine learning potentials (MLPs) for reactant and
             product states, which is redundant and inefficient. In this work, an
             automated workflow is developed to construct a universal MLP for both
             states, which can compute the redox potentials or acidity constants of
             redox-active organic molecules more efficiently. Furthermore, the
             predicted redox potentials can be evaluated at the hybrid functional
             level with much lower costs, which would facilitate the design of
             aqueous organic RFBs.},
}

@Article{Liu_JApplPhys_2024_v135,
    author =   {Xiangjun Liu and Baolong Wang and Kun Jia and Quanjie Wang and Di Wang
             and Yucheng Xiong},
    title =    {{First-principles-based machine learning interatomic potential for
             molecular dynamics simulations of 2D lateral MoS2/WS2 heterostructures}},
    journal =  {J. Appl. Phys.},
    year =     2024,
    volume =   135,
    number =   20,
    doi =      {10.1063/5.0201527},
    abstract = {<jats:p>Understanding the mechanical and
             thermodynamic properties of transition-metal dichalcogenides (TMDs)
             and their heterostructures is pivotal for advancing the development of
             flexible semiconductor devices, and molecular dynamics (MD) simulation
             is widely applied to study these properties. However, current
             uncertainties persist regarding the efficacy of empirical potentials
             in MD simulations to accurately describe the intricate performance of
             complex interfaces within heterostructures. This study addresses these
             challenges by developing an interatomic potential based on deep neural
             networks and first-principles calculations. Specifically focusing on
             MoS2/WS2 heterostructures, our approach aims to predict Young's
             modulus and thermal conductivities. The potential's effectiveness is
             demonstrated through the validation of structural features, mechanical
             properties, and thermodynamic characteristics, revealing close
             alignment with values derived from first-principles calculations. A
             noteworthy finding is the substantial influence of the load direction
             on Young's modulus of heterostructures. Furthermore, our results
             highlight that the interfacial thermal conductance of the MoS2/WS2
             heterostructures is considerably larger than that of graphene-based
             interfaces. The potential developed in this work facilitates large-
             scale material simulations, bridging the gap with first-principles
             calculations. Notably, it outperforms empirical potentials under
             interface conditions, establishing its significant competitiveness in
             simulation computations. Our approach not only contributes to a deeper
             understanding of TMDs and heterostructures but also presents a robust
             tool for the simulation of their mechanical and thermal behaviors,
             paving the way for advancements in flexible semiconductor device
             manufacturing.</jats:p>},
}

@Article{Yokaichiya_JChemPhys_2024_v160_p204108,
    author =   {Tomoko Yokaichiya and Tatsushi Ikeda and Koki Muraoka and Akira
             Nakayama},
    title =    {{On-the-fly kinetic Monte Carlo simulations with neural network
             potentials for surface diffusion and reaction}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   20,
    pages =    204108,
    doi =      {10.1063/5.0199240},
    abstract = {We develop an adaptive scheme in the kinetic Monte Carlo simulations,
             where the adsorption and activation energies of all elementary steps,
             including the effects of other adsorbates, are evaluated {''}on-the-
             fly{''} by employing the neural network potentials. The configurations
             and energies evaluated during the simulations are stored for reuse
             when the same configurations are sampled in a later step. The present
             scheme is applied to hydrogen adsorption and diffusion on the Pd(111)
             and Pt(111) surfaces and the CO oxidation reaction on the Pt(111)
             surface. The effects of interactions between adsorbates, i.e.,
             adsorbate-adsorbate lateral interactions, are examined in detail by
             comparing the simulations without considering lateral interactions.
             This study demonstrates the importance of lateral interactions in
             surface diffusion and reactions and the potential of our scheme for
             applications in a wide variety of heterogeneous catalytic reactions.},
}

@Article{David_JAmChemSoc_2024_v146_p14213,
    author =   {Rolf David and I{\~n}aki Tu{\~n}{\'o}n and Damien Laage},
    title =    {{Competing Reaction Mechanisms of Peptide Bond Formation in Water
             Revealed by Deep Potential Molecular Dynamics and Path Sampling}},
    journal =  {J. Am. Chem. Soc.},
    year =     2024,
    volume =   146,
    number =   20,
    pages =    {14213--14224},
    doi =      {10.1021/jacs.4c03445},
    abstract = {The formation of an amide bond is an essential step in the synthesis
             of materials and drugs, and in the assembly of amino acids to form
             peptides. The mechanism of this reaction has been studied extensively,
             in particular to understand how it can be catalyzed, but a
             representation capable of explaining all the experimental data is
             still lacking. Numerical simulation should provide the necessary
             molecular description, but the solvent involvement poses a number of
             challenges. Here, we combine the efficiency and accuracy of neural
             network potential-based reactive molecular dynamics with the extensive
             and unbiased exploration of reaction pathways provided by transition
             path sampling. Using microsecond-scale simulations at the density
             functional theory level, we show that this method reveals the presence
             of two competing distinct mechanisms for peptide bond formation
             between alanine esters in aqueous solution. We describe how both
             reaction pathways, via a general base catalysis mechanism and via
             direct cleavage of the tetrahedral intermediate respectively, change
             with pH. This result contrasts with the conventional mechanism
             involving a single pathway in which only the barrier heights are
             affected by pH. We show that this new proposal involving two competing
             mechanisms is consistent with the experimental data, and we discuss
             the implications for peptide bond formation under prebiotic conditions
             and in the ribosome. Our work shows that integrating deep potential
             molecular dynamics with path sampling provides a powerful approach for
             exploring complex chemical mechanisms.},
}

@Article{Que_JChemPhys_2024_v160_p194710,
    author =   {Zhi-Xiong Que and Shu-Zong Li and Bo Huang and Zhi-Xiong Yang and Wei-
             Bing Zhang},
    title =    {{Ultra-flat bands at large twist angles in group-V twisted bilayer
             materials}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   19,
    pages =    194710,
    doi =      {10.1063/5.0197757},
    abstract = {Flat bands in 2D twisted materials are key to the realization of
             correlation-related exotic phenomena. However, a flat band often was
             achieved in the large system with a very small twist angle, which
             enormously increases the computational and experimental complexity. In
             this work, we proposed group-V twisted bilayer materials, including P,
             As, and Sb in the {\ensuremath{\beta}} phase with large twist angles.
             The band structure of twisted bilayer materials up to 2524 atoms has
             been investigated by a deep learning method DeepH, which significantly
             reduces the computational time. Our results show that the bandgap and
             the flat bandwidth of twisted bilayer {\ensuremath{\beta}}-P,
             {\ensuremath{\beta}}-As, and {\ensuremath{\beta}}-Sb reduce gradually
             with the decreasing of twist angle, and the ultra-flat band with
             bandwidth approaching 0{~}eV is achieved. Interestingly, we found that
             a twist angle of 9.43{\textdegree} is sufficient to achieve the band
             flatness for {\ensuremath{\beta}}-As comparable to that of twist
             bilayer graphene at the magic angle of 1.08{\textdegree}. Moreover, we
             also find that the bandgap reduces with decreasing interlayer distance
             while the flat band is still preserved, which suggests interlayer
             distance as an effective routine to tune the bandgap of flat band
             systems. Our research provides a feasible platform for exploring
             physical phenomena related to flat bands in twisted layered 2D
             materials.},
}

@Article{Guo_JChemPhys_2024_v160_p174313,
    author =   {Longfei Guo and Shuang Shan and Xiaoqing Liu and Wanxuan Zhang and
             Peng Xu and Fanzhe Ma and Zhen Li and Chongyang Wang and Junpeng Wang
             and Fuyi Chen},
    title =    {{Revealing the reconstruction mechanism of AgPd nanoalloys under
             fluorination based on a multiscale deep learning potential}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   17,
    pages =    174313,
    doi =      {10.1063/5.0205616},
    abstract = {The design of heterogeneous catalysts generally involves optimizing
             the reactivity descriptor of adsorption energy, which is inevitably
             governed by the structure of surface-active sites. A prerequisite for
             understanding the structure-properties relationship is the precise
             identification of real surface-active site structures, rather than
             relying on conceived structures derived from bulk alloy properties.
             However, it remains a formidable challenge due to the dynamic nature
             of nanoalloys during catalytic reactions and the lack of accurate and
             efficient interatomic potentials for simulations. Herein, a
             generalizable deep-learning potential for the Ag-Pd-F system is
             developed based on a dataset encompassing the bulk, surface,
             nanocluster, amorphous, and point defected configurations with diverse
             compositions to achieve a comprehensive description of interatomic
             interactions, facilitating precise prediction of adsorption energy,
             surface energy, formation energy, and diffusion energy barrier and is
             utilized to investigate the structural evolutions of AgPd nanoalloys
             during fluorination. The structural evolutions involve the inward
             diffusion of F, the outward diffusion of Ag in Ag@Pd nanoalloys, the
             formation of surface AgFx species in mixed and Janus AgPd nanoalloys,
             and the shape deformation from cuboctahedron to sphere in Ag and Pd@Ag
             nanoalloys. Moreover, the effects of atomic diffusion and dislocation
             formation and migration on the reconstructing pathway of nanoalloys
             are highlighted. It is demonstrated that the stress relaxation upon F
             adsorption serves as the intrinsic driving factor governing the
             surface reconstruction of AgPd nanoalloys.},
}

@Article{Omranpour_JChemPhys_2024_v160_p170901,
    author =   {Amir Omranpour and Pablo {\{}Montero De Hijes{\}} and J{\"o}rg Behler
             and Christoph Dellago},
    title =    {{Perspective: Atomistic simulations of water and aqueous systems with
             machine learning potentials}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   17,
    pages =    170901,
    doi =      {10.1063/5.0201241},
    abstract = {As the most important solvent, water has been at the center of
             interest since the advent of computer simulations. While early
             molecular dynamics and Monte Carlo simulations had to make use of
             simple model potentials to describe the atomic interactions, accurate
             ab{~}initio molecular dynamics simulations relying on the first-
             principles calculation of the energies and forces have opened the way
             to predictive simulations of aqueous systems. Still, these simulations
             are very demanding, which prevents the study of complex systems and
             their properties. Modern machine learning potentials (MLPs) have now
             reached a mature state, allowing us to overcome these limitations by
             combining the high accuracy of electronic structure calculations with
             the efficiency of empirical force fields. In this Perspective, we give
             a concise overview about the progress made in the simulation of water
             and aqueous systems employing MLPs, starting from early work on free
             molecules and clusters via bulk liquid water to electrolyte solutions
             and solid-liquid interfaces.},
}

@Article{Zhang_AcsMaterLett_2024_v6_p1849,
    author =   {Dexin Zhang and Yiwei You and Fulun Wu and Xinrui Cao and Tie-Yu
             L{\"u} and Yang Sun and Zi-Zhong Zhu and Shunqing Wu},
    title =    {{Exploring the Relationship between Composition and Li-Ion Conductivity
             in the Amorphous Li{\textendash}La{\textendash}Zr{\textendash}O System}},
    journal =  {Acs Mater. Lett,},
    year =     2024,
    volume =   6,
    number =   5,
    pages =    {1849--1855},
    doi =      {10.1021/acsmaterialslett.3c01558},
}

@Article{Shi_JPhysChemA_2024_v128_p3449,
    author =   {Zhiyu Shi and Aditya Dilip Lele and Ahren W. Jasper and Stephen J.
             Klippenstein and Yiguang Ju},
    title =    {{Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab
             Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data}},
    journal =  {J. Phys. Chem., A},
    year =     2024,
    volume =   128,
    number =   17,
    pages =    {3449--3457},
    doi =      {10.1021/acs.jpca.4c00750},
    abstract = {Machine learning (ML) provides a great opportunity for the
             construction of models with improved accuracy in classical molecular
             dynamics (MD). However, the accuracy of a ML trained model is limited
             by the quality and quantity of the training data. Generating large
             sets of accurate ab initio training data can require significant
             computational resources. Furthermore, inconsistent or incompatible
             data with different accuracies obtained using different methods may
             lead to biased or unreliable ML models that do not accurately
             represent the underlying physics. Recently, transfer learning showed
             its potential for avoiding these problems as well as for improving the
             accuracy, efficiency, and generalization of ML models using
             multifidelity data. In this work, ab initio trained ML-based MD (aML-
             MD) models are developed through transfer learning using DFT and
             multireference data from multiple sources with varying accuracy within
             the Deep Potential MD framework. The accuracy of the force field is
             demonstrated by calculating rate constants for the H + HO2
             {\textrightarrow} H2 + 3O2 reaction using quasi-classical
             trajectories. We show that the aML-MD model with transfer learning can
             accurately predict the rate constants while reducing the computational
             cost by more than five times compared to the use of more expensive
             quantum chemistry training data sets. Hence, the aML-MD model with
             transfer learning shows great potential in using multifidelity data to
             reduce the computational cost involved in generating the training set
             for these potentials.},
}

@Article{Selvaraj_JElectrochemSoc_2024_v171_p50544,
    author =   {Selva Chandrasekaran Selvaraj and Volodymyr Koverga and Anh T. Ngo},
    title =    {{Exploring Li-Ion Transport Properties of Li<sub{\ensure
             math{>}}3</sub>TiCl<sub{\
             ensuremath{>}}6</sub>: A Machine
             Learning Molecular Dynamics Study}},
    journal =  {J. Electrochem., Soc,},
    year =     2024,
    volume =   171,
    number =   5,
    pages =    50544,
    doi =      {10.1149/1945-7111/ad4ac9},
    abstract = {<jats:p>We performed large-scale
             molecular dynamics simulations based on a machine-learning force field
             (MLFF) to investigate the Li-ion transport mechanism in cation-
             disordered Li<jats:sub>3<
             /jats:sub>TiCl<jats:sub>6
             </jats:sub> cathode at six different
             temperatures, ranging from 25{\textdegree}C to 100{\textdegree}C. In
             this work, deep neural network method and data generated by ab
             {\ensuremath{-}} initio molecular dynamics (AIMD) simulations were
             deployed to build a high-fidelity MLFF. Radial distribution functions,
             Li-ion mean square displacements (MSD), diffusion coefficients, ionic
             conductivity, activation energy, and crystallographic direction-
             dependent migration barriers were calculated and compared with
             corresponding AIMD and experimental data to benchmark the accuracy of
             the MLFF. From MSD analysis, we captured both the self and distinct
             parts of Li-ion dynamics. The latter reveals that the Li-ions are
             involved in anti-correlation motion that was rarely reported for
             solid-state materials. Similarly, the self and distinct parts of Li-
             ion dynamics were used to determine Haven{\textquoteright}s ratio to
             describe the Li-ion transport mechanism in Li<jats:sub{
             \ensuremath{>}}3</jats:sub>TiCl{\ensurem
             ath{<}}jats:sub>6</jats:sub{\ensuremath{
             >}}. Obtained trajectory from molecular dynamics infers that the Li-
             ion transportation is mainly through interstitial hopping which was
             confirmed by intra- and inter-layer Li-ion displacement with respect
             to simulation time. Ionic conductivity (1.06 mS/cm) and activation
             energy (0.29eV) calculated by our simulation are highly comparable
             with that of experimental values. Overall, the combination of machine-
             learning methods and AIMD simulations explains the intricate
             electrochemical properties of the Li<jats:sub{\ensurema
             th{>}}3</jats:sub>TiCl<ja
             ts:sub>6</jats:sub>
             cathode with remarkably reduced computational time. Thus, our work
             strongly suggests that the deep neural network-based MLFF could be a
             promising method for large-scale complex
             materials.</jats:p>},
}

@Article{Li_PhysRevB_2024_v109_p184108,
    author =   {Zhi Li and Sandro Scandolo},
    title =    {{Deep-learning interatomic potential for iron at extreme conditions}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   18,
    pages =    184108,
    doi =      {10.1103/PhysRevB.109.184108},
}

@Article{Shi_PhysRevB_2024_v109_p174104,
    author =   {Yubai Shi and Ri He and Bingwen Zhang and Zhicheng Zhong},
    title =    {{Revisiting the phase diagram and piezoelectricity of lead zirconate
             titanate from first principles}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   17,
    pages =    174104,
    doi =      {10.1103/PhysRevB.109.174104},
}

@Article{Zhang_PhysRevB_2024_v109_p174106,
    author =   {Xi Zhang and Yu-Tian Zhang and Yun-Peng Wang and Shiyu Li and Shixuan
             Du and Yu-Yang Zhang and Sokrates T. Pantelides},
    title =    {{Structural and mechanical properties of monolayer amorphous carbon and
             boron nitride}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   17,
    pages =    174106,
    doi =      {10.1103/PhysRevB.109.174106},
    abstract = {Amorphous materials exhibit various characteristics that are not
             featured by crystals and can sometimes be tuned by their degree of
             disorder (DOD). Here, we report results on the mechanical properties
             of monolayer amorphous carbon (MAC) and monolayer amorphous boron
             nitride (maBN) with different DOD. The pertinent structures are
             obtained by kinetic-Monte-Carlo (kMC) simulations using machine-
             learning potentials (MLP) with density-functional-theory (DFT)-level
             accuracy. An intuitive order parameter, namely the areal fraction Fx
             occupied by crystallites within the continuous random network, is
             proposed to describe the DOD. We find that Fx captures the essence of
             the DOD: Samples with the same Fx but different sizes and
             distributions of crystallites have virtually identical radial
             distributions functions as well as bond-length and bond-angle
             distributions. Furthermore, by simulating the fracture process with
             molecular dynamics, we found that the mechanical responses of MAC and
             maBN before fracture are solely determined by Fx and are insensitive
             to the sizes and specific arrangements of the crystallites. The
             behavior of cracks in the two materials is analyzed and found to
             mainly propagate in meandering paths in the CRN region and to be
             influenced by crystallites in distinct ways that toughen the material.
             The present results reveal the relation between structure and
             mechanical properties in amorphous monolayers and may provide a
             universal toughening strategy for 2D materials.},
}

@Article{Shi_ExtremMechLett_2024_v68_p102151,
    author =   {Pengjie Shi and Shizhe Feng and Zhiping Xu},
    title =    {{Non-equilibrium nature of fracture determines the crack paths}},
    journal =  {Extrem. Mech. Lett.},
    year =     2024,
    volume =   68,
    pages =    102151,
    doi =      {10.1016/j.eml.2024.102151},
}

@Article{Xiao_BioresourTechnol_2024_v399_p130590,
    author =   {Yuqin Xiao and Yuxin Yan and Hainam Do and Richard Rankin and Haitao
             Zhao and Ping Qian and Keke Song and Tao Wu and Cheng Heng Pang},
    title =    {{Understanding cellulose pyrolysis via ab initio deep learning
             potential field}},
    journal =  {Bioresour. Technol.},
    year =     2024,
    volume =   399,
    pages =    130590,
    doi =      {10.1016/j.biortech.2024.130590},
    abstract = {Comprehensive and dynamic studies of cellulose pyrolysis reaction
             mechanisms are crucial in designing experiments and processes with
             enhanced safety, efficiency, and sustainability. The details of the
             pyrolysis mechanism are not readily available from experiments but can
             be better described via molecular dynamics (MD) simulations. However,
             the large size of cellulose molecules challenges accurate ab initio MD
             simulations, while existing reactive force field parameters lack
             precision. In this work, precise ab initio deep learning potentials
             field (DPLF) are developed and applied in MD simulations to facilitate
             the study of cellulose pyrolysis mechanisms. The formation mechanism
             and production rate of both valuable and greenhouse products from
             cellulose at temperatures larger than 1073{~}K are comprehensively
             described. This study underscores the critical role of advanced
             simulation techniques, particularly DLPF, in achieving efficient and
             accurate understanding of cellulose pyrolysis mechanisms, thus
             promoting wider industrial applications.},
}

@Article{Mirchi_PhysChemChemPhys_2024_v26_p14216,
    author =   {Pedram Mirchi and Christophe Adessi and Samy Merabia and Ali Rajabpour},
    title =    {{Lattice thermal conductivity and mechanical properties of the single-
             layer penta-NiN2 explored by a deep-learning interatomic potential}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   19,
    pages =    {14216--14227},
    doi =      {10.1039/d4cp00997e},
    abstract = {Penta-NiN2, a novel pentagonal 2D sheet with potential nanoelectronic
             applications, is investigated in terms of its lattice thermal
             conductivity, stability, and mechanical behavior. A deep learning
             interatomic potential (DLP) is firstly generated from ab initio
             molecular dynamics (AIMD) data and then utilized for classical
             molecular dynamics simulations. The DLP's accuracy is verified,
             showing strong agreement with AIMD results. The dependence of thermal
             conductivity on size, temperature, and tensile strain, reveals
             important insights into the material's thermal properties.
             Additionally, the mechanical response of penta-NiN2 under uniaxial
             loading is examined, yielding a Young's modulus of approximately 368
             GPa. The influence of vacancy defects on mechanical properties is
             analyzed, demonstrating a significant reduction in modulus, fracture
             stress, and ultimate strength. This study also investigates the
             influence of strain on phonon dispersion relations and phonon group
             velocity in penta-NiN2, shedding light on how alterations in the
             atomic lattice affect the phonon dynamics and, consequently, impact
             the thermal conductivity. This investigation showcases the ability of
             deep learning-based interatomic potentials in studying the properties
             of 2D penta-NiN2.},
}

@Article{Dong_JApplPhys_2024_v135,
    author =   {Haikuan Dong and Yongbo Shi and Penghua Ying and Ke Xu and Ting Liang
             and Yanzhou Wang and Zezhu Zeng and Xin Wu and Wenjiang Zhou and
             Shiyun Xiong and Shunda Chen and Zheyong Fan},
    title =    {{Molecular dynamics simulations of heat transport using machine-learned
             potentials: A mini-review and tutorial on GPUMD with neuroevolution
             potentials}},
    journal =  {J. Appl. Phys.},
    year =     2024,
    volume =   135,
    number =   16,
    doi =      {10.1063/5.0200833},
    abstract = {<jats:p>Molecular dynamics (MD)
             simulations play an important role in understanding and engineering
             heat transport properties of complex materials. An essential
             requirement for reliably predicting heat transport properties is the
             use of accurate and efficient interatomic potentials. Recently,
             machine-learned potentials (MLPs) have shown great promise in
             providing the required accuracy for a broad range of materials. In
             this mini-review and tutorial, we delve into the fundamentals of heat
             transport, explore pertinent MD simulation methods, and survey the
             applications of MLPs in MD simulations of heat transport. Furthermore,
             we provide a step-by-step tutorial on developing MLPs for highly
             efficient and predictive heat transport simulations, utilizing the
             neuroevolution potentials as implemented in the GPUMD package. Our aim
             with this mini-review and tutorial is to empower researchers with
             valuable insights into cutting-edge methodologies that can
             significantly enhance the accuracy and efficiency of MD simulations
             for heat transport studies.</jats:p>},
}

@Article{Balyakin_ComputMaterSci_2024_v239_p112979,
    author =   {I.A. Balyakin and M.I. Vlasov and S.V. Pershina and D.M. Tsymbarenko
             and A.A. Rempel},
    title =    {{Neural network molecular dynamics study of LiGe2(PO4)3: Investigation
             of structure}},
    journal =  {Comput. Mater. Sci.},
    year =     2024,
    volume =   239,
    pages =    112979,
    doi =      {10.1016/j.commatsci.2024.112979},
}

@Article{Ghaffari_ComputMaterSci_2024_v239_p112983,
    author =   {Kimia Ghaffari and Salil Bavdekar and Douglas E. Spearot and Ghatu
             Subhash},
    title =    {{Validation workflow for machine learning interatomic potentials for
             complex ceramics}},
    journal =  {Comput. Mater. Sci.},
    year =     2024,
    volume =   239,
    pages =    112983,
    doi =      {10.1016/j.commatsci.2024.112983},
}

@Article{Zhu_ComputMaterSci_2024_v239_p112966,
    author =   {Chang-sheng Zhu and Wen-jing Dong and Zi-hao Gao and Li-jun Wang and
             Guang-zhao Li},
    title =    {{Deep Potential fitting and mechanical properties study of MgAlSi alloy}},
    journal =  {Comput. Mater. Sci.},
    year =     2024,
    volume =   239,
    pages =    112966,
    doi =      {10.1016/j.commatsci.2024.112966},
}

@Article{Zhu_JPhysChemLett_2024_v15_p4024,
    author =   {Da Zhu and Li Sheng and Taiping Hu and Sian Chen and Mengchao Shi and
             Haiming Hua and Kai Yang and Jianlong Wang and Yaping Tang and
             Xiangming He and Hong Xu},
    title =    {{Investigation of the Degradation of LiPF6- in Polar Solvents through
             Deep Potential Molecular Dynamics}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2024,
    volume =   15,
    number =   15,
    pages =    {4024--4030},
    doi =      {10.1021/acs.jpclett.4c00575},
    abstract = {The nonaqueous electrolyte based on lithium hexafluorophosphate
             (LiPF6) is the dominant liquid electrolyte in lithium-ion batteries
             (LIBs). However, trace protic impurities, including H3O+, alcohols,
             and hydrofluoric acid (HF), can trigger a series of side reactions
             that lead to rapid capacity fading in high energy density LIBs. It is
             worth noting that this degradation process is highly dependent on the
             polarity of the solvents. In this work, a deep potential (DP) model is
             trained with a certain commercial electrolyte formula through a
             machine learning method. H3O+ is anchored with polar solvents, making
             it difficult to approach the PF6-, and suppressing the degradation
             process quickly at room temperature. Control experiments and
             simulations at different temperatures or concentrations are also
             performed to verify it. This work proposes a precise model to describe
             the solvation structure quantitatively and offers a new perspective on
             the degradation mechanism of PF6- in polar solvents.},
}

@Article{Zills_JPhysChemB_2024_v128_p3662,
    author =   {Fabian Zills and Moritz Ren{\'e} Sch{\"a}fer and Nico Segreto and
             Johannes K{\"a}stner and Christian Holm and Samuel Tovey},
    title =    {{Collaboration on Machine-Learned Potentials with IPSuite: A Modular
             Framework for Learning-on-the-Fly}},
    journal =  {J. Phys. Chem., B},
    year =     2024,
    volume =   128,
    number =   15,
    pages =    {3662--3676},
    doi =      {10.1021/acs.jpcb.3c07187},
    abstract = {The field of machine learning potentials has experienced a rapid surge
             in progress, thanks to advances in machine learning theory,
             algorithms, and hardware capabilities. While the underlying methods
             are continuously evolving, the infrastructure for their deployment has
             lagged. The community, due to these rapid developments, frequently
             finds itself split into groups built around different implementations
             of machine-learned potentials. In this work, we introduce IPSuite, a
             Python-driven software package designed to connect different methods
             and algorithms from the comprehensive field of machine-learned
             potentials into a single platform while also providing a collaborative
             infrastructure, helping ensure reproducibility. Furthermore, the data
             management infrastructure of the IPSuite code enables simple model
             sharing and deployment in simulations. Currently, IPSuite supports six
             state-of-the-art machine learning approaches for the fitting of
             interatomic potentials as well as a variety of methods for the
             selection of training data, running of ab initio calculations,
             learning-on-the-fly strategies, model evaluation, and simulation
             deployment.},
}

@Article{Zhou_AcsApplMaterInterfaces_2024_v16_p18874,
    author =   {Rui Zhou and Kun Luo and Steve W. Martin and Qi An},
    title =    {{Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic
             Conductivity via Machine Learning Force Field Simulations}},
    journal =  {Acs Appl. Mater. Interfaces},
    year =     2024,
    volume =   16,
    number =   15,
    pages =    {18874--18887},
    doi =      {10.1021/acsami.4c00618},
    abstract = {Sulfide-based solid electrolytes (SEs) are important for advancing
             all-solid-state batteries (ASSBs), primarily due to their high ionic
             conductivities and robust mechanical stability. Glassy SEs (GSEs)
             comprising mixed Si and P glass formers are particularly promising for
             their synthesis process and their ability to prevent lithium dendrite
             growth. However, to date, the complexity of their glassy structures
             hinders a complete understanding of the relationships between their
             structures and properties. This study introduces a new machine
             learning force field (ML-FF) tailored for lithium sulfide-based GSEs,
             enabling the exploration of their structural characteristics,
             mechanical properties, and lithium ionic conductivities. Using
             molecular dynamic (MD) simulations with this ML-FF, we explore the
             glass structures in varying compositions, including binary Li2S-SiS2
             and Li2S-P2S5 as well as ternary Li2S-SiS2-P2S5. Our simulations
             yielded consistent results in terms of density, elastic modulus,
             radial distribution functions, and neutron structure factors compared
             to DFT and experimental work. Our findings reveal distinct local
             environments for Si and P within these glasses, with most Si atoms in
             edge-sharing configurations in Li2S-SiS2 and a mix of corner- and
             edge-sharing tetrahedra in the ternary Li2S-SiS2-P2S5 composition. For
             lithium ionic conductivity at 300 K, the 50Li2S-50SiS2 glass displayed
             the lowest conductivity at 2.1 mS/cm, while the 75Li2S-25P2S5
             composition exhibited the highest conductivity at 3.6 mS/cm. The
             ternary glass showed a conductivity of 2.6 mS/cm, sitting between the
             two. Moreover, an in-depth analysis of lithium ion diffusion over the
             MD trajectory in the ternary glass demonstrated a significant
             correlation between diffusion pathways and the rotational dynamics of
             nearby SiS4 or PS4 tetrahedra. The ML-FF developed in this study
             provides an important tool for exploring a broad spectrum of solid-
             state and mixed former sulfide-based electrolytes.},
}

@Article{Zhang_InorgChem_2024_v63_p6743,
    author =   {Rongyu Zhang and Shifeng Xu and Liyan Wang and Chuanyun Wang and
             Yongjun Zhou and Zhe L{\"u} and Wenbo Li and Dan Xu and Sai Wang and
             Xu Yang},
    title =    {{Theoretical Study on Ion Diffusion Mechanism in W-Doped K3SbS4 as
             Solid-State Electrolyte for K-Ion Batteries}},
    journal =  {Inorg. Chem.},
    year =     2024,
    volume =   63,
    number =   15,
    pages =    {6743--6751},
    doi =      {10.1021/acs.inorgchem.4c00074},
    abstract = {The development of a solid-state electrolyte (SSE) is crucial for
             overcoming the side reactions of metal potassium anodes and advancing
             the progress of K-ion batteries (KIBs). Exploring the diffusion
             mechanism of the K ion in SSE is important for deepening our
             understanding and promoting its development. In this study, we
             conducted static calculations and utilized deep potential molecular
             dynamics (DeepMD) to investigate the behavior of cubic K3SbS4. The
             original K3SbS4 exhibited poor ionic conductivity, but we discovered
             that introducing heterovalent tungsten doping created vacancies, which
             significantly reduced the activation energy to 0.12 eV and enhanced
             the ionic conductivity to 1.80 {\texttimes} 10-2 S/cm. The diffusion
             of K-ions in K3SbS4 primarily occurs through the exchange of positions
             with K vacancies. This research provides insights into the design of
             SSE with high ionic conductivity. Furthermore, it highlights the
             effectiveness of DeepMD as a powerful tool for studying the SSE.},
}

@Article{Woo_MaterLett_2024_v361_p136114,
    author =   {Sung Hun Woo and Hyun Joo Yang and Yongseon Kim},
    title =    {{Investigation of the effect of off-stoichiometric composition on
             oxygen transport in layered perovskite materials for SOFC cathode}},
    journal =  {Mater. Lett.},
    year =     2024,
    volume =   361,
    pages =    136114,
    doi =      {10.1016/j.matlet.2024.136114},
}

@Article{Feng_ChemEngSci_2024_v288_p119836,
    author =   {Taixi Feng and Bo Yang and Jia Zhao and Guimin Lu},
    title =    {{Elucidating the local structure and properties of molten Na2CO3-K2CO3
             salts using Machine Learning-Driven molecular dynamics}},
    journal =  {Chem. Eng. Sci.},
    year =     2024,
    volume =   288,
    pages =    119836,
    doi =      {10.1016/j.ces.2024.119836},
}

@Article{Zhang_CeramInt_2024_v50_p13740,
    author =   {Hanchao Zhang and Guoliang Ren and Peng Jia and Xiaofeng Zhao and Na
             Ni},
    title =    {{Development of machine learning force field for thermal conductivity
             analysis in MoAlB: Insights into anisotropic heat transfer mechanisms}},
    journal =  {Ceram. Int.},
    year =     2024,
    volume =   50,
    number =   8,
    pages =    {13740--13749},
    doi =      {10.1016/j.ceramint.2024.01.288},
}

@Article{Pan_JComputChem_2024_v45_p638,
    author =   {Xiaoliang Pan and Ryan Snyder and Jia-Ning Wang and Chance Lander and
             Carly Wickizer and Richard Van and Andrew Chesney and Yuanfei Xue and
             Yuezhi Mao and Ye Mei and Jingzhi Pu and Yihan Shao},
    title =    {{Training machine learning potentials for reactive systems: A Colab
             tutorial on basic models}},
    journal =  {J. Comput. Chem.},
    year =     2024,
    volume =   45,
    number =   10,
    pages =    {638--647},
    doi =      {10.1002/jcc.27269},
    abstract = {In the last several years, there has been a surge in the development
             of machine learning potential (MLP) models for describing molecular
             systems. We are interested in a particular area of this field - the
             training of system-specific MLPs for reactive systems - with the goal
             of using these MLPs to accelerate free energy simulations of chemical
             and enzyme reactions. To help new members in our labs become familiar
             with the basic techniques, we have put together a self-guided Colab
             tutorial (https://cc-ats.github.io/mlp{\_}tutorial/), which we expect
             to be also useful to other young researchers in the community. Our
             tutorial begins with the introduction of simple feedforward neural
             network (FNN) and kernel-based (using Gaussian process regression,
             GPR) models by fitting the two-dimensional M{\"u}ller-Brown potential.
             Subsequently, two simple descriptors are presented for extracting
             features of molecular systems: symmetry functions (including the ANI
             variant) and embedding neural networks (such as DeepPot-SE). Lastly,
             these features will be fed into FNN and GPR models to reproduce the
             energies and forces for the molecular configurations in a Claisen
             rearrangement reaction.},
}

@Article{Chen_JApplPhys_2024_v135,
    author =   {Rongkun Chen and Yu Tian and Jiayi Cao and Weina Ren and Shiqian Hu
             and Chunhua Zeng},
    title =    {{Unified deep learning network for enhanced accuracy in predicting
             thermal conductivity of bilayer graphene, hexagonal boron nitride, and
             their heterostructures}},
    journal =  {J. Appl. Phys.},
    year =     2024,
    volume =   135,
    number =   14,
    doi =      {10.1063/5.0201698},
    abstract = {<jats:p>In this research, we utilized
             density functional theory (DFT) computations to perform ab initio
             molecular dynamics simulations and static calculations on graphene,
             hexagonal boron nitride, and their heterostructures, subjecting them
             to strains, perturbations, twist angles, and defects. The gathered
             energy, force, and virial information informed the creation of a
             training set comprising 1253 structures. Employing the Neural
             Evolutionary Potential framework integrated into Graphics Processing
             Units Molecular Dynamics, we fitted a machine learning potential (MLP)
             that closely mirrored the DFT potential energy surface. Rigorous
             validation of lattice constants and phonon dispersion relations
             confirmed the precision and dependability of the MLP, establishing a
             solid foundation for subsequent thermal transport investigations. A
             further analysis of the impact of twist angles uncovered a significant
             reduction in thermal conductivity, particularly notable in
             heterostructures with a decline exceeding 35{\%}. The reduction in
             thermal conductivity primarily stems from the twist angle-induced
             softening of phonon modes and the accompanying increase in phonon
             scattering rates, which intensifies anharmonic interactions among
             phonons. Our study underscores the efficacy of the MLP in delineating
             the thermal transport attributes of two-dimensional materials and
             their heterostructures, while also elucidating the micro-mechanisms
             behind the influence of the twist angle on thermal conductivity,
             offering fresh perspectives for the design of advanced thermal
             management materials.</jats:p>},
}

@Article{Zhai_JChemPhys_2024_v160_p144501,
    author =   {Yaoguang Zhai and Richa Rashmi and Etienne Palos and Francesco Paesani},
    title =    {{Many-body interactions and deep neural network potentials for water}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   14,
    pages =    144501,
    doi =      {10.1063/5.0203682},
    abstract = {We present a detailed assessment of deep neural network potentials
             developed within the Deep Potential Molecular Dynamics (DeePMD)
             framework and trained on the MB-pol data-driven many-body potential
             energy function. Specific focus is directed at the ability of DeePMD-
             based potentials to correctly reproduce the accuracy of MB-pol across
             various water systems. Analyses of bulk and interfacial properties as
             well as many-body interactions characteristic of water elucidate
             inherent limitations in the transferability and predictive accuracy of
             DeePMD-based potentials. These limitations can be traced back to an
             incomplete implementation of the {''}nearsightedness of electronic
             matter{''} principle, which may be common throughout machine learning
             potentials that do not include a proper representation of self-
             consistently determined long-range electric fields. These findings
             provide further support for the {''}short-blanket dilemma{''} faced by
             DeePMD-based potentials, highlighting the challenges in achieving a
             balance between computational efficiency and a rigorous, physics-based
             representation of the properties of water. Finally, we believe that
             our study contributes to the ongoing discourse on the development and
             application of machine learning models in simulating water systems,
             offering insights that could guide future improvements in the field.},
}

@Article{Maxson_JPhysChemLett_2024_v15_p3740,
    author =   {Tristan Maxson and Tibor Szilv{\'a}si},
    title =    {{Transferable Water Potentials Using Equivariant Neural Networks}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2024,
    volume =   15,
    number =   14,
    pages =    {3740--3747},
    doi =      {10.1021/acs.jpclett.4c00605},
    abstract = {Machine learning interatomic potentials (MLIPs) have emerged as a
             technique that promises quantum theory accuracy for reduced cost. It
             has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs
             trained on solely liquid water data cannot accurately transfer to the
             vapor-liquid equilibrium while recovering the many-body decomposition
             (MBD) analysis of gas-phase water clusters. This suggests that MLIPs
             do not directly learn the physically correct interactions of water
             molecules, limiting transferability. In this work, we show that MLIPs
             using equivariant architecture and trained on 3200 liquid water
             structures reproduces liquid-phase water properties (e.g., density
             within 0.003 g/cm3 between 230 and 365 K), vapor-liquid equilibrium
             properties up to 550 K, the MBD analysis of gas-phase water cluster up
             to six-body interactions, and the relative energy and the vibrational
             density of states of ice phases. We show that potentials developed
             using equivariant MLIPs allow transferability for arbitrary phases of
             water that remain stable in nanosecond long simulations.},
}

@Article{Miyagawa_JMaterChemA_2024_v12_p11344,
    author =   {Takeru Miyagawa and Namita Krishnan and Manuel Grumet and Christian
             Rever{\'o}n Baecker and Waldemar Kaiser and David A. Egger},
    title =    {{Accurate description of ion migration in solid-state ion conductors
             from machine-learning molecular dynamics}},
    journal =  {J. Mater. Chem. A},
    year =     2024,
    volume =   12,
    number =   19,
    pages =    {11344--11361},
    doi =      {10.1039/d4ta00452c},
    abstract = {<jats:p>Machine-learning molecular
             dynamics provides predictions of structural and anharmonic vibrational
             properties of solid-state ionic conductors with
             <jats:italic>ab
             initio</jats:italic> accuracy. This
             opens a path towards rapid design of novel battery
             materials.</jats:p>},
}

@Article{Ying_AcsNano_2024_v18_p10133,
    author =   {Penghua Ying and Amir Natan and Oded Hod and Michael Urbakh},
    title =    {{Effect of Interlayer Bonding on Superlubric Sliding of Graphene
             Contacts: A Machine-Learning Potential Study}},
    journal =  {Acs Nano},
    year =     2024,
    volume =   18,
    number =   14,
    pages =    {10133--10141},
    doi =      {10.1021/acsnano.3c13099},
    abstract = {Surface defects and their mutual interactions are anticipated to
             affect the superlubric sliding of incommensurate layered material
             interfaces. Atomistic understanding of this phenomenon is limited due
             to the high computational cost of ab initio simulations and the
             absence of reliable classical force-fields for molecular dynamics
             simulations of defected systems. To address this, we present a
             machine-learning potential (MLP) for bilayer defected graphene,
             utilizing state-of-the-art graph neural networks trained against many-
             body dispersion corrected density functional theory calculations under
             iterative configuration space exploration. The developed MLP is
             utilized to study the impact of interlayer bonding on the friction of
             bilayer defected graphene interfaces. While a mild effect on the
             sliding dynamics of aligned graphene interfaces is observed, the
             friction coefficients of incommensurate graphene interfaces are found
             to significantly increase due to interlayer bonding, nearly pushing
             the system out of the superlubric regime. The methodology utilized
             herein is of general nature and can be adapted to describe other
             homogeneous and heterogeneous defected layered material interfaces.},
}

@Article{Zhang_ApplPhysLett_2024_v124,
    author =   {Jingwen Zhang and Junjie Zhang and Guoqiang Bao and Zehan Li and
             Xiaobo Li and Te-Huan Liu and Ronggui Yang},
    title =    {{Prediction of phonon properties of cubic boron nitride with vacancy
             defects and isotopic disorders by using a neural network potential}},
    journal =  {Appl. Phys. Lett.},
    year =     2024,
    volume =   124,
    number =   15,
    doi =      {10.1063/5.0198431},
    abstract = {<jats:p>Cubic boron nitride (c-BN) is a
             promising ultra-wide bandgap semiconductor for high-power electronic
             devices. Its thermal conductivity can be substantially modified by
             controlling the isotope abundance and by the quality of a single
             crystal. Consequently, an understanding of the phonon transport in
             c-BN crystals, with both vacancy defects and isotopic disorders at
             near-ambient temperatures, is of practical importance. In the present
             study, a neural network potential (NNP) for c-BN has been developed,
             which has facilitated the investigation of phonon properties under
             these circumstances. As a result, the phonon dispersion and the three-
             and four-phonon scattering rates that were predicted with this NNP
             were in close agreement with those obtained from density-functional
             theory (DFT) calculations. The thermal conductivities of the c-BN
             crystals were also investigated, with boron (B) vacancies ranging from
             0.0{\%} to 0.6{\%}, by using equilibrium molecular dynamics
             simulations based on the Green-Kubo formula. These simulations
             accurately capture vacancy-induced phonon softening, localized
             vibration modes, and phonon localization effects. As has previously
             been experimentally prepared, four isotope-modified c-BN samples were
             selected for analyses in the evaluation of the impact of isotopic
             disorders. The calculated thermal conductivities aligned well with the
             DFT benchmarks. In addition, the present study was extended to include
             a c-BN crystal with a natural abundance of B atoms, which also
             contained B vacancies. Reasonable thermal conductivities and
             vibrational characteristics, within the temperature range of
             250{\textendash}500{\,}K, were then
             obtained.</jats:p>},
}

@Article{Xu_JPhysChemC_2024_v128_p5697,
    author =   {Sen Xu and Liling Wu and Yi Fan and Yufeng Liu and Xiongzhi Zeng and
             Zhenyu Li},
    title =    {{Hydrocarbon Species on the Cu(111) Surface Studied with a Neural
             Network Potential}},
    journal =  {J. Phys. Chem. C},
    year =     2024,
    volume =   128,
    number =   13,
    pages =    {5697--5707},
    doi =      {10.1021/acs.jpcc.3c08138},
}

@Article{Kobayashi_ChemSci_2024_v15_p6816,
    author =   {Taro Kobayashi and Tatsushi Ikeda and Akira Nakayama},
    title =    {{Long-range proton and hydroxide ion transfer dynamics at the
             water/CeO2 interface in the nanosecond regime: reactive molecular
             dynamics simulations and kinetic analysis}},
    journal =  {Chem. Sci.},
    year =     2024,
    volume =   15,
    number =   18,
    pages =    {6816--6832},
    doi =      {10.1039/d4sc01422g},
    abstract = {The structural properties, dynamical behaviors, and ion transport
             phenomena at the interface between water and cerium oxide are
             investigated by reactive molecular dynamics (MD) simulations employing
             neural network potentials (NNPs). The NNPs are trained to reproduce
             density functional theory (DFT) results, and DFT-based MD (DFT-MD)
             simulations with enhanced sampling techniques and refinement schemes
             are employed to efficiently and systematically acquire training data
             that include diverse hydrogen-bonding configurations caused by proton
             hopping events. The water interfaces with two low-index surfaces of
             (111) and (110) are explored with these NNPs, and the structure and
             long-range proton and hydroxide ion transfer dynamics are examined
             with unprecedented system sizes and long simulation times. Various
             types of proton hopping events at the interface are categorized and
             analyzed in detail. Furthermore, in order to decipher the proton and
             hydroxide ion transport phenomena along the surface, a counting
             analysis based on the semi-Markov process is formulated and applied to
             the MD trajectories to obtain reaction rates by considering the
             transport as stochastic jump processes. Through this model, the
             coupling between hopping events, vibrational motions, and hydrogen
             bond networks at the interface are quantitatively examined, and the
             high activity and ion transport phenomena at the water/CeO2 interface
             are unequivocally revealed in the nanosecond regime.},
}

@Article{Hsing_PhysRevMater_2024_v8_p43806,
    author =   {Cheng-Rong Hsing and Duc-Long Nguyen and Ching-Ming Wei},
    title =    {{Exploring diffusion behavior of superionic materials using machine-
             learning interatomic potentials}},
    journal =  {Phys, Rev, Mater.},
    year =     2024,
    volume =   8,
    number =   4,
    pages =    43806,
    doi =      {10.1103/PhysRevMaterials.8.043806},
}

@Article{Agrawal_Nanoscale_2024_v16_p8986,
    author =   {Sraddha Agrawal and Bipeng Wang and Yifan Wu and David Casanova and
             Oleg V. Prezhdo},
    title =    {{Photocatalytic activity of dual defect modified graphitic carbon
             nitride is robust to tautomerism: machine learning assisted ab initio
             quantum dynamics}},
    journal =  {Nanoscale},
    year =     2024,
    volume =   16,
    number =   18,
    pages =    {8986--8995},
    doi =      {10.1039/d4nr00606b},
    abstract = {Two-dimensional graphitic carbon nitride (GCN) is a popular metal-free
             polymer for sustainable energy applications due to its unique
             structure and semiconductor properties. Dopants and defects are used
             to tune GCN, and dual defect modified GCN exhibits superior properties
             and enhanced photocatalytic efficiency in comparison to pristine or
             single defect GCN. We employ a multistep approach combining time-
             dependent density functional theory and nonadiabatic molecular
             dynamics (NAMD) with machine learning (ML) to investigate coupled
             structural and electronic dynamics in GCN over a nanosecond timescale,
             comparable to and exceeding the lifetimes of photo-generated charge
             carriers and photocatalytic events. Although frequent hydrogen hopping
             transitions occur among four tautomeric structures, the electron-hole
             separation and recombination processes are only weakly sensitive to
             the tautomerism. The charge separated state survives for about 10 ps,
             sufficiently long to enable photocatalysis. The employed ML-NAMD
             methodology provides insights into rare events that can influence
             excited state dynamics in the condensed phase and nanoscale materials
             and extends NAMD simulations from pico- to nanoseconds. The ab initio
             quantum dynamics simulation provides a detailed atomistic mechanism of
             photoinduced evolution of charge carriers in GCN and rationalizes how
             GCN remains photo-catalytically active despite its multiple isomeric
             and tautomeric forms.},
}

@Article{Peng_JgrSolidEarth_2024_v129,
    author =   {Yihang Peng and Jie Deng},
    title =    {{Hydrogen Diffusion in the Lower Mantle Revealed by Machine Learning
             Potentials}},
    journal =  {Jgr Solid Earth},
    year =     2024,
    volume =   129,
    number =   4,
    doi =      {10.1029/2023JB028333},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>Hydrogen
             may be incorporated into nominally anhydrous minerals including
             bridgmanite and post{-}perovskite as defects, making the Earth's deep
             mantle a potentially significant water reservoir. The diffusion of
             hydrogen and its contribution to the electrical conductivity in the
             lower mantle are rarely explored and remain largely unconstrained.
             Here we calculate hydrogen diffusivity in hydrous bridgmanite and
             post{-}perovskite, using molecular dynamics simulations driven by
             machine learning potentials of ab initio quality. Our findings reveal
             that hydrogen diffusivity significantly increases with increasing
             temperature and decreasing pressure, and is considerably sensitive to
             hydrogen incorporation mechanism. Among the four defect mechanisms
             examined, (Mg{~}+{~}2H)<jats:sub>Si{\ens
             uremath{<}}/jats:sub> and (Al{~}+{~}H)<j
             ats:sub>Si</jats:sub>
             show similar patterns and yield the highest hydrogen diffusivity.
             Hydrogen diffusion is generally faster in post{-}perovskite than in
             bridgmanite, and these two phases exhibit distinct diffusion
             anisotropies. Overall, hydrogen diffusion is slow on geological time
             scales and may result in heterogeneous water distribution in the lower
             mantle. Additionally, the proton conductivity of bridgmanite for (Mg{~
             }+{~}2H)<jats:sub>Si</jat
             s:sub> and (Al{~}+{~}H)<jats:sub{\ensure
             math{>}}Si</jats:sub> defects aligns
             with the same order of magnitude of lower mantle conductivity,
             suggesting that the water distribution in the lower mantle may be
             inferred by examining the heterogeneity of electrical
             conductivity.</jats:p>},
}

@Article{Du_NatlSciRev_2024_v11_pnwae023,
    author =   {Tao Du and Shanwu Li and Sudheer Ganisetti and Mathieu Bauchy and
             Yuanzheng Yue and Morten M. Smedskjaer},
    title =    {{Deciphering the controlling factors for phase transitions in zeolitic
             imidazolate frameworks}},
    journal =  {Natl. Sci. Rev.},
    year =     2024,
    volume =   11,
    number =   4,
    pages =    {nwae023},
    doi =      {10.1093/nsr/nwae023},
    abstract = {Zeolitic imidazolate frameworks (ZIFs) feature complex phase
             transitions, including polymorphism, melting, vitrification, and
             polyamorphism. Experimentally probing their structural evolution
             during transitions involving amorphous phases is a significant
             challenge, especially at the medium-range length scale. To overcome
             this challenge, here we first train a deep learning-based force field
             to identify the structural characteristics of both crystalline and
             non-crystalline ZIF phases. This allows us to reproduce the structural
             evolution trend during the melting of crystals and formation of ZIF
             glasses at various length scales with an accuracy comparable to that
             of ab initio molecular dynamics, yet at a much lower computational
             cost. Based on this approach, we propose a new structural descriptor,
             namely, the ring orientation index, to capture the propensity for
             crystallization of ZIF-4 (Zn(Im)2, Im{~}={~}C3H3N2-) glasses, as well
             as for the formation of ZIF-zni (Zn(Im)2) out of the high-density
             amorphous phase. This crystal formation process is a result of the
             reorientation of imidazole rings by sacrificing the order of the
             structure around the zinc-centered tetrahedra. The outcomes of this
             work are useful for studying phase transitions in other metal-organic
             frameworks (MOFs) and may thus guide the development of MOF glasses.},
}

@Article{Prasnikar_ArtifIntellRev_2024_v57_p102,
    author =   {Eva Pra{\v{s}}nikar and Martin Ljubi{\v{c}} and Andrej Perdih and Jure
             Bori{\v{s}}ek},
    title =    {{Machine learning heralding a new development phase in molecular
             dynamics simulations}},
    journal =  {Artif Intell Rev},
    year =     2024,
    volume =   57,
    number =   4,
    pages =    102,
    doi =      {10.1007/s10462-024-10731-4},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>Molecular
             dynamics (MD) simulations are a key computational chemistry technique
             that provide dynamic insight into the underlying atomic-level
             processes in the system under study. These insights not only improve
             our understanding of the molecular world, but also aid in the design
             of experiments and targeted interventions. Currently, MD is associated
             with several limitations, the most important of which are:
             insufficient sampling, inadequate accuracy of the atomistic models,
             and challenges with proper analysis and interpretation of the obtained
             trajectories. Although numerous efforts have been made to address
             these limitations, more effective solutions are still needed. The
             recent development of artificial intelligence, particularly machine
             learning (ML), offers exciting opportunities to address the challenges
             of MD. In this review we aim to familiarize readers with the basics of
             MD while highlighting its limitations. The main focus is on exploring
             the integration of deep learning with MD simulations. The advancements
             made by ML are systematically outlined, including the development of
             ML-based force fields, techniques for improved conformational space
             sampling, and innovative methods for trajectory analysis.
             Additionally, the challenges and implications associated with the
             integration of ML and artificial intelligence are discussed. While the
             potential of ML-MD fusion is clearly established, further applications
             are needed to confirm its superiority over traditional methods. This
             comprehensive overview of the new perspectives of MD, which ML has
             opened up, serves as a gentle introduction to the exciting phase of MD
             development.</jats:p>},
}

@Article{Zhang_SciChinaMater_2024_v67_p1129,
    author =   {Cun Zhang and Bolin Yang and Zhilong Peng and Shaohua Chen},
    title =    {{Machine learning-based prediction of mechanical properties of N-doped $\gamma$-graphdiyne
}},
    journal =  {Sci. China Mater.},
    year =     2024,
    volume =   67,
    number =   4,
    pages =    {1129--1139},
    doi =      {10.1007/s40843-023-2733-7},
}

@Article{Fu_JNuclMater_2024_v591_p154897,
    author =   {Baoqin Fu and Yandong Sun and Wanrun Jiang and Fu Wang and Linfeng
             Zhang and Han Wang and Ben Xu},
    title =    {{Determining the thermal conductivity and phonon behavior of SiC
             materials with quantum accuracy via deep learning interatomic
             potential model}},
    journal =  {J. Nucl. Mater.},
    year =     2024,
    volume =   591,
    pages =    154897,
    doi =      {10.1016/j.jnucmat.2024.154897},
}

@Article{Obeid_JEnergyStorage_2024_v82_p110587,
    author =   {Mohammed M. Obeid and Jiahui Liu and Penghu Du and Tongyu Liu and
             Qiang Sun},
    title =    {{A 3D metallic porous sulfurized carbon anode identified by global
             structure search for Na-ion batteries with fast diffusion kinetics}},
    journal =  {J. Energy Storage},
    year =     2024,
    volume =   82,
    pages =    110587,
    doi =      {10.1016/j.est.2024.110587},
}

@Article{Xu_JApplPhys_2024_v135,
    author =   {Fei-Yang Xu and Zhi-Guo Li and Xiang-Rong Chen and Hua Y. Geng and Lei
             Liu and Jianbo Hu},
    title =    {{Probing the critical point of MgSiO3 using deep potential simulation}},
    journal =  {J. Appl. Phys.},
    year =     2024,
    volume =   135,
    number =   12,
    doi =      {10.1063/5.0189696},
    abstract = {<jats:p>The giant impact between proto-
             Earth and a Mars-sized planet called Theia resulted in the formation
             of the Earth{\textendash}Moon system, and the silicate mantles of the
             initial bodies may have partly been vaporized. Here, we develop a
             machine learning potential for MgSiO3 based on the data from first-
             principles calculations to estimate its critical point. The variations
             in pressure along different isotherms yield the position of the
             critical point of MgSiO3 at 0.54{\,}g{\,}cm{\ensuremath{-}}3 and
             6750{\,}{\ensuremath{\pm}}{\,}250{\,}K, which agrees with the previous
             theoretical estimation. We also simulate the MgSiO3 melt under a
             spectrum of critical conditions to understand the changes in
             coordination environment with density and temperature. The fourfold
             Si{\textendash}O coordination hardly changes with increasing density
             at 3000{\,}K. However, with increasing temperature, the dominance of
             four-coordinated Si{\textendash}O diminishes rapidly as density
             decreases. Regarding Mg{\textendash}O coordination, the overall trend,
             which varies with temperature and density, remains largely consistent
             with Si{\textendash}O but with a greater diversity in the types of
             coordination due to more bond breaking events. Our work opens a new
             avenue by employing machine learning methods to estimate the critical
             point of silicates.</jats:p>},
}

@Article{Chang_AcsApplMaterInterfaces_2024_v16_p14954,
    author =   {Xiaoya Chang and Yongchao Wu and Qingzhao Chu and Gang Zhang and
             Dongping Chen},
    title =    {{Ab Initio Driven Exploration on the Thermal Properties of Al-Li Alloy}},
    journal =  {Acs Appl. Mater. Interfaces},
    year =     2024,
    volume =   16,
    number =   12,
    pages =    {14954--14964},
    doi =      {10.1021/acsami.4c01480},
    abstract = {Al-Li alloys are feasible and promising additives in advanced energy
             and propellant systems due to the significantly enhanced heat release
             and increased specific impulse. The thermal properties of Al-Li alloys
             directly determine the manufacturing, storage safety, and ignition
             delay of propellants. In this study, a neural network potential (NNP)
             is developed to investigate the thermal behaviors of Al-Li alloys from
             an atomistic perspective. The novel NNP demonstrates an excellent
             predictive ability for energy, atomic force, mechanical behaviors,
             phonon vibrations, and dynamic evolutions. A series of NNP-based
             molecular dynamics simulations are performed to investigate the effect
             of Li doping on the thermal properties of Al-Li alloys. All calculated
             results for Al-Li alloys are consistent with experimental values for
             Al, ensuring their validity in predicting Al-Li interactions. The
             simulation results suggest that a minor increment in the Li content
             results in a slight change in the melting point, thermal expansion,
             and radical distribution functions. These three properties are
             associated with the lattice characteristics; nonetheless, it causes a
             substantial reduction in thermal conductivity, which is related to the
             physical properties of the elements. The lower thermal conductivity
             allows heat accumulation on the particle surface, thereby speeding up
             the surface premelt and ignition. This provides an alternative atomic
             explanation for the improved combustion performance of Al-Li alloys.
             These findings integrate insights from the field of alloy material
             science into crucial combustion applications, serving as an atomistic
             guide for developing manufacturing techniques.},
}

@Article{Li_PhysChemChemPhys_2024_v26_p12044,
    author =   {Xuejiao Li and Tingrui Xu and Yu Gong},
    title =    {{Compositional transferability of deep potential in molten LiF-BeF2 and
             LaF3 mixtures: prediction of density, viscosity, and local structure}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   15,
    pages =    {12044--12052},
    doi =      {10.1039/d4cp00079j},
    abstract = {The accumulation of lanthanide fission products carries the risk of
             altering the structure and properties of the nuclear fuel carrier salt
             LiF-BeF2 (Flibe), thereby downgrading the operating efficiency and
             safety of the molten salt reactor. However, the condition-limited
             experimental measurements, spatiotemporal-limited first-principles
             calculations, and accuracy-limited classical dynamic simulations are
             unable to capture the precise local structure and reliable
             thermophysical properties of heterogeneous molten salts. Therefore,
             the deep potential (DP) of LaF3 and Flibe molten mixtures is developed
             here, and DP molecular dynamics simulations are performed to
             systemically study the densities, diffusion coefficients, viscosities,
             radial distribution functions and coordination numbers of multiple
             molten Flibe + xLaF3, the quantitative relationships between these
             properties and LaF3 concentration are investigated, and the potential
             structure-property relationships are analyzed. Eventually, the
             transferability of DP on molten Flibe + LaF3 with different
             formulations as well as the predictability of structures and
             properties are achieved at the nanometer spatial scale and the
             nanosecond timescale.},
}

@Article{Liu_ChemMaterPublAmChemSoc_2024_v36_p2898,
    author =   {Dongyu Liu and Yifan Wu and Mikhail R. Samatov and Andrey S. Vasenko
             and Evgueni V. Chulkov and Oleg V. Prezhdo},
    title =    {{Compression Eliminates Charge Traps by Stabilizing Perovskite Grain
             Boundary Structures: An Ab Initio Analysis with Machine Learning Force
             Field}},
    journal =  {Chem. Mater.: Publ. Am. Chem. Soc.},
    year =     2024,
    volume =   36,
    number =   6,
    pages =    {2898--2906},
    doi =      {10.1021/acs.chemmater.3c03261},
    abstract = {Grain boundaries (GBs) play an important role in determining the
             optoelectronic properties of perovskites, requiring an atomistic
             understanding of the underlying mechanisms. Strain engineering has
             recently been employed in perovskite solar cells, providing a novel
             perspective on the role of perovskite GBs. Here, we theoretically
             investigate the impact of axial strain on the geometric and electronic
             properties of a common CsPbBr3 GB. We develop a machine learning force
             field and perform ab initio calculations to analyze the behavior of GB
             models with different axial strains on a nanosecond time scale. Our
             results demonstrate that compressing the GB efficiently suppresses
             structural fluctuations and eliminates trap states originating from
             large-scale distortions. The GB becomes more amorphous under
             compressive strain, which makes the relationship between the
             electronic structure and axial strain nonmonotonic. These results can
             help clarify the conflicts in perovskite GB experiments.},
}

@Article{delaPuente_JPhysChemLett_2024_v15_p3096,
    author =   {Miguel {\{}de la Puente{\}} and Axel Gomez and Damien Laage},
    title =    {{Neural Network-Based Sum-Frequency Generation Spectra of Pure and
             Acidified Water Interfaces with Air}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2024,
    volume =   15,
    number =   11,
    pages =    {3096--3102},
    doi =      {10.1021/acs.jpclett.4c00113},
    abstract = {The affinity of hydronium ions (H3O+) for the air-water interface is a
             crucial question in environmental chemistry. While sum-frequency
             generation (SFG) spectroscopy has been instrumental in indicating the
             preference of H3O+ for the interface, key questions persist regarding
             the molecular origin of the SFG spectral changes in acidified water.
             Here we combine nanosecond long neural network (NN) reactive
             simulations of pure and acidified water slabs with NN predictions of
             molecular dipoles and polarizabilities to calculate SFG spectra of
             long reactive trajectories including proton transfer events. Our
             simulations show that H3O+ ions cause two distinct changes in phase-
             resolved SFG spectra: first, a low-frequency tail due to the
             vibrations of H3O+ and its first hydration shell, analogous to the
             bulk proton continuum, and second, an enhanced hydrogen-bonded band
             due to the ion-induced static field polarizing molecules in deeper
             layers. Our calculations confirm that changes in the SFG spectra of
             acidic solutions are caused by hydronium ions preferentially residing
             at the interface.},
}

@Article{Xiao_PhysChemChemPhys_2024_v26_p11867,
    author =   {Hang Xiao and Bin Yang},
    title =    {{A neural network potential energy surface assisted molecular dynamics
             study on the pyrolysis behavior of two spiro-hydrocarbons}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   15,
    pages =    {11867--11879},
    doi =      {10.1039/d3cp05425j},
    abstract = {Spiro-hydrocarbons are potentially a type of novel alternative jet
             fuel due to their high density and net heat of combustion. In this
             work, the pyrolysis study of two spiro-hydrocarbons
             (spiro[cyclopropane-1,6'-tricyclo[3.2.1.02,4]octane] (C10H14) as Fuel
             1 and spiro[bicyclo[2.2.1]heptane-2,1'-cyclopropane] (C9H14) as Fuel
             2) is performed via molecular dynamics (MD) simulations, with a neural
             network potential energy surface (NNPES), deep potential (DP) model,
             adopted. The data set for the DP model of each fuel is constructed
             after 31 and 27 iterations, respectively. The high precision of the DP
             model is demonstrated, and the temperature transferability of each
             model is observed. The overall pyrolysis performance is evaluated with
             the fuel decomposition rate, showing that both fuels have comparable
             gas-reactivity to commercial aviation fuels, such as JP-10. The
             reaction networks of initial pyrolysis for Fuels 1 and 2 are
             constructed, and the contribution of each pathway is discussed. Fuel 1
             tends to form an unsaturated six-membered ring structure, while Fuel 2
             generates unsaturated open-chain hydrocarbons. Further analyses of the
             MD results provide time-evolution information on each component in the
             pyrolysis species pool. Compared to Fuel 1, the initial pyrolysis of
             Fuel 2 leads to more hydrogen, alkenes, and alkanes, as well as fewer
             monocyclic aromatic hydrocarbons (MAHs), demonstrating a reduced
             tendency for afterward coking. This work might contribute to the
             development of the mechanism of the two spiro-hydrocarbons and guide
             the research of other similar structural fuels.},
}

@Article{Peng_GeophysResLett_2024_v51,
    author =   {Yihang Peng and Jie Deng},
    title =    {{Thermal Conductivity of MgSiO<sub>3{\ens
             uremath{<}}/sub>{-
             }H<sub>2</sub{\ensuremath
             {>}}O System Determined by Machine Learning Potentials}},
    journal =  {Geophys. Res. Lett.},
    year =     2024,
    volume =   51,
    number =   5,
    doi =      {10.1029/2023GL107245},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>Thermal
             conductivity plays a pivotal role in understanding the dynamics and
             evolution of Earth's interior. The Earth's lower mantle is dominated
             by MgSiO<jats:sub>3</jats
             :sub> polymorphs which may incorporate trace amounts of
             water. However, the thermal conductivity of MgSiO<jats:
             sub>3</jats:sub>{-
             }H<jats:sub>2</jats:sub{\
             ensuremath{>}}O binary system remains poorly understood. Here, we
             calculate the thermal conductivity of water{-}free and water{-}bearing
             bridgmanite, post{-}perovskite, and MgSiO<jats:sub{\ens
             uremath{>}}3</jats:sub> melt, using a
             combination of Green{-}Kubo method with molecular dynamics simulations
             based on a machine learning potential of ab initio quality. The
             thermal conductivities of water{-}free bridgmanite and
             post{-}perovskite overall agree well with previous theoretical and
             experimental studies. The presence of water mildly reduces the thermal
             conductivity of the host minerals, significantly weakens the
             temperature dependence of the thermal conductivity, and reduces the
             thermal anisotropy of post{-}perovskite. Overall, water reduces the
             thermal conductivity difference between bridgmanite and
             post{-}perovskite, and thus may attenuate lateral heterogeneities of
             the core{-}mantle boundary heat
             flux.</jats:p>},
}

@Article{Pang_PhysChemChemPhys_2024_v26_p11545,
    author =   {Kehui Pang and Mingjie Wen and Xiaoya Chang and Yabei Xu and Qingzhao
             Chu and Dongping Chen},
    title =    {{The thermal decomposition mechanism of RDX/AP composites: ab initio
             neural network MD simulations}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   15,
    pages =    {11545--11557},
    doi =      {10.1039/d3cp05709g},
    abstract = {A neural network potential (NNP) is developed to investigate the
             decomposition mechanism of RDX, AP, and their composites. Utilizing an
             ab initio dataset, the NNP is evaluated in terms of atomic energy and
             forces, demonstrating strong agreement with ab initio calculations.
             Numerical stability tests across a range of timesteps reveal excellent
             stability compared to the state-of-the-art ReaxFF models. Then the
             thermal decomposition of pure RDX, AP, and RDX/AP composites is
             performed using NNP to explore the coupling effect between RDX and AP.
             The results highlight a dual interaction between RDX and AP, i.e., AP
             accelerates RDX decomposition, particularly at low temperatures, and
             RDX promotes AP decomposition. Analyzing RDX trajectories at the
             RDX/AP interface unveils a three-part decomposition mechanism
             involving N-N bond cleavage, H transfer with AP to form Cl-containing
             acid, and chain-breaking reactions generating small molecules such as
             N2, CO, and CO2. The presence of AP enhances H transfer reactions,
             contributing to its role in promoting RDX decomposition. This work
             studies the reaction kinetics of RDX/AP composites from the atomic
             point of view, and can be widely used in the establishment of reaction
             kinetics models of composite systems with energetic materials.},
}

@Article{Gan_PhysRevB_2024_v109_p115129,
    author =   {Bo Gan and Jun Li and Junjie Gao and Qiru Zeng and Wenhao Song and
             Yukai Zhuang and Yingxin Hua and Qiang Wu and Gang Jiang and Yuan Yin
             and Youjun Zhang},
    title =    {{Electrical conductivity of copper under ultrahigh pressure and
             temperature conditions by both experiments and first-principles
             simulations}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   11,
    pages =    115129,
    doi =      {10.1103/PhysRevB.109.115129},
}

@Article{Soshnikov_JChemPhys_2024_v160_p094117,
    author =   {Artem Soshnikov and Rebecca Lindsey and Ambarish Kulkarni and Nir
             Goldman},
    title =    {{A reactive molecular dynamics model for uranium/hydrogen containing
             systems}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   9,
    pages =    094117,
    doi =      {10.1063/5.0183610},
    abstract = {Uranium-based materials are valuable assets in the energy, medical,
             and military industries. However, understanding their sensitivity to
             hydrogen embrittlement is particularly challenging due to the toxicity
             of uranium and the computationally expensive nature of quantum-based
             methods generally required to study such processes. In this regard, we
             have developed a Chebyshev Interaction Model for Efficient Simulation
             (ChIMES) that can be employed to compute energies and forces of U and
             UH3 bulk structures with vacancies and hydrogen interstitials with
             accuracy similar to that of Density Functional Theory (DFT) while
             yielding linear scaling and orders of magnitude improvement in
             computational efficiency. We show that the bulk structural parameters,
             uranium and hydrogen vacancy formation energies, and diffusion
             barriers predicted by the ChIMES potential are in strong agreement
             with the reference DFT data. We then use ChIMES to conduct molecular
             dynamics simulations of the temperature-dependent diffusion of a
             hydrogen interstitial and determine the corresponding diffusion
             activation energy. Our model has particular significance in studies of
             actinides and other high-Z materials, where there is a strong need for
             computationally efficient methods to bridge length and time scales
             between experiments and quantum theory.},
}

@Article{Liu_JPhysChemA_2024_v128_p1656,
    author =   {Ziyi Liu and An-Hui Lu and Dongqi Wang},
    title =    {{Deep Potential Molecular Dynamics Study of Propane Oxidative
             Dehydrogenation}},
    journal =  {J. Phys. Chem., A},
    year =     2024,
    volume =   128,
    number =   9,
    pages =    {1656--1664},
    doi =      {10.1021/acs.jpca.3c07859},
    abstract = {Oxidative dehydrogenation (ODH) of light alkanes is a key process in
             the oxidative conversion of alkanes to alkenes, oxygenated
             hydrocarbons, and COx (x = 1,2). Understanding the underlying
             mechanisms extensively is crucial to keep the ODH under control for
             target products, e.g., alkenes rather than COx, with minimal energy
             consumption, e.g., during the alkene production or maximal energy
             release, e.g., during combustion. In this work, deep potential (DP), a
             neural network atomic potential developed in recent years, was
             employed to conduct large-scale accurate reactive dynamic simulations.
             The model was trained on a sufficient data set obtained at the density
             functional theory level. The intricate reaction network was elucidated
             and organized in the form of a hierarchical network to demonstrate the
             key features of the ODH mechanisms, including the activation of
             propane and oxygen, the influence of propyl reaction pathways on the
             propene selectivity, and the role of rapid H2O2 decomposition for
             sustainable and efficient ODH reactions. The results indicate the more
             complex reaction mechanism of propane ODH than that of ethane ODH and
             are expected to provide insights in the ODH catalyst optimization. In
             addition, this work represents the first application of deep potential
             in the ODH mechanistic study and demonstrates the ample advantages of
             DP in the study of mechanism and dynamics of complex systems.},
}

@Article{Ojih_JMaterChemA_2024_v12_p8502,
    author =   {Joshua Ojih and Mohammed Al-Fahdi and Yagang Yao and Jianjun Hu and
             Ming Hu},
    title =    {{Graph theory and graph neural network assisted high-throughput crystal
             structure prediction and screening for energy conversion and storage}},
    journal =  {J. Mater. Chem. A},
    year =     2024,
    volume =   12,
    number =   14,
    pages =    {8502--8515},
    doi =      {10.1039/d3ta06190f},
    abstract = {<jats:p>Prediction of crystal structures
             with desirable material properties is a grand challenge in materials
             research. We deployed graph theory assisted structure searcher and
             combined with universal machine learning potentials to accelerate the
             process.</jats:p>},
}

@Article{Ren_EnergyEnvSci_2024_v17_p2743,
    author =   {Fucheng Ren and Yuqi Wu and Wenhua Zuo and Wengao Zhao and Siyuan Pan
             and Hongxin Lin and Haichuan Yu and Jing Lin and Min Lin and Xiayin
             Yao and Torsten Brezesinski and Zhengliang Gong and Yong Yang},
    title =    {{Visualizing the SEI formation between lithium metal and solid-state
             electrolyte}},
    journal =  {Energy Env., Sci,},
    year =     2024,
    volume =   17,
    number =   8,
    pages =    {2743--2752},
    doi =      {10.1039/d3ee03536k},
    abstract = {<jats:p>Large-scale molecular dynamics
             simulations reveal the formation mechanism and structure of the solid
             electrolyte interphase between lithium metal and {\ensuremath{\beta}}-
             Li<jats:sub>3</jats:sub{\
             ensuremath{>}}PS<jats:sub>4{\ensuremath{
             <}}/jats:sub> in all-solid-state
             batteries.</jats:p>},
}

@Article{Liu_ChemSci_2024_v15_p5294,
    author =   {Shanping Liu and Romain Dupuis and Dong Fan and Salma Benzaria and
             Mickaele Bonneau and Prashant Bhatt and Mohamed Eddaoudi and Guillaume
             Maurin},
    title =    {{Machine learning potential for modelling H2 adsorption/diffusion in
             MOFs with open metal sites}},
    journal =  {Chem. Sci.},
    year =     2024,
    volume =   15,
    number =   14,
    pages =    {5294--5302},
    doi =      {10.1039/d3sc05612k},
    abstract = {Metal-organic frameworks (MOFs) incorporating open metal sites (OMS)
             have been identified as promising sorbents for many societally
             relevant-adsorption applications including CO2 capture, natural gas
             purification and H2 storage. This has been ascribed to strong specific
             interactions between OMS and the guest molecules that enable the MOF
             to achieve an effective capture even under low gas pressure
             conditions. In particular, the presence of OMS in MOFs was
             demonstrated to substantially boost the H2 binding energy for
             achieving high adsorbed hydrogen densities and large usable hydrogen
             capacities. So far, there is a critical bottleneck to computationally
             attain a full understanding of the thermodynamics and dynamics of H2
             in this sub-class of MOFs since the generic classical force fields
             (FFs) are known to fail to accurately describe the interactions
             between OMS and any guest molecules, in particular H2. This clearly
             hampers the computational-assisted identification of MOFs containing
             OMS for a target adsorption-related application since the standard
             high-throughput screening approach based on these generic FFs is not
             applicable. Therefore, there is a need to derive novel FFs to achieve
             accurate and effective evaluation of MOFs for H2 adsorption. On this
             path, as a proof-of-concept, the soc-MOF-1d containing OMS, previously
             envisaged as a potential platform for H2 adsorption, was selected as a
             benchmark material and a machine learning potential (MLP) was derived
             for the Al-soc-MOF-1d from a dataset initially generated by ab initio
             molecular dynamics (AIMD) simulations. This MLP was further
             implemented in MD simulations to explore the H2 binding modes as well
             as the temperature dependence distribution of H2 in the MOF pores from
             10 K to 80 K. MLP-Grand Canonical Monte Carlo (GCMC) simulations were
             then performed to predict the H2 sorption isotherm of Al-soc-MOF-1d at
             77 K that was further confirmed using sorption data we collected on
             this sample. As a further step, MLP-based molecular dynamics (MD)
             simulations were conducted to anticipate the kinetics of H2 in this
             MOF. This work delivers the first MLP able to describe accurately the
             interactions between the challenging H2 guest molecule and MOFs
             containing OMS. This innovative strategy applied to one of the most
             complex molecules owing to its highly polarizable nature, paves the
             way towards a more systematic accurate and efficient in silico
             assessment of MOFs containing OMS for H2 adsorption and beyond to the
             low-pressure capture of diverse molecules.},
}

@Article{Urata_PhysRevMater_2024_v8_p33602,
    author =   {Shingo Urata and Aik Rui Tan and Rafael G{\'o}mez-Bombarelli},
    title =    {{Modifying ring structures in lithium borate glasses under compression:
             MD simulations using a machine-learning potential}},
    journal =  {Phys, Rev, Mater.},
    year =     2024,
    volume =   8,
    number =   3,
    pages =    33602,
    doi =      {10.1103/PhysRevMaterials.8.033602},
}

@Article{Wan_PhysRevB_2024_v109_p94101,
    author =   {Tianqi Wan and Chenxing Luo and Yang Sun and Renata M. Wentzcovitch},
    title =    {{Thermoelastic properties of bridgmanite using deep-potential molecular
             dynamics}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   9,
    pages =    94101,
    doi =      {10.1103/PhysRevB.109.094101},
    abstract = {MgSiO{\_}3-perovskite (MgPv) plays a crucial role in the Earth's lower
             mantle. This study combines deep-learning potential (DP) with density
             functional theory (DFT) to investigate the structural and elastic
             properties of MgPv under lower mantle conditions. To simulate complex
             systems, we developed a series of potentials capable of faithfully
             reproducing DFT calculations using different functionals, such as LDA,
             PBE, PBEsol, and SCAN meta-GGA functionals. The obtained predictions
             exhibit remarkable reliability and consistency, closely resembling
             experimental measurements. Our results highlight the superior
             performance of the DP-SCAN and DP-LDA in accurately predicting high-
             temperature equations of states and elastic properties. This hybrid
             computational approach offers a solution to the accuracy-efficiency
             dilemma in obtaining precise elastic properties at high pressure and
             temperature conditions for minerals like MgPv, which opens a new way
             to study the Earth's interior state and related processes.},
}

@Article{Ryu_JMolLiq_2024_v397_p124054,
    author =   {Jae Hyun Ryu and Ji Woong Yu and Tae Jun Yoon and Won Bo Lee},
    title =    {{Understanding the dielectric relaxation of liquid water using neural
             network potential and classical pairwise potential}},
    journal =  {J. Mol. Liq.},
    year =     2024,
    volume =   397,
    pages =    124054,
    doi =      {10.1016/j.molliq.2024.124054},
}

@Article{Gardner_MachLearnSciTechnol_2024_v5_p15003,
    author =   {John L A Gardner and Kathryn T Baker and Volker L Deringer},
    title =    {{Synthetic pre-training for neural-network interatomic potentials}},
    journal =  {Mach, Learn.,: Sci, Technol,},
    year =     2024,
    volume =   5,
    number =   1,
    pages =    15003,
    doi =      {10.1088/2632-2153/ad1626},
    abstract = {<jats:title>Abstract</jat
             s:title>
             <jats:p>Machine learning (ML) based
             interatomic potentials have transformed the field of atomistic
             materials modelling. However, ML potentials depend critically on the
             quality and quantity of quantum-mechanical reference data with which
             they are trained, and therefore developing datasets and training
             pipelines is becoming an increasingly central challenge. Leveraging
             the idea of {\textquoteleft}synthetic{\textquoteright} (artificial)
             data that is common in other areas of ML research, we here show that
             synthetic atomistic data, themselves obtained at scale with an
             existing ML potential, constitute a useful pre-training task for
             neural-network (NN) interatomic potential models. Once pre-trained
             with a large synthetic dataset, these models can be fine-tuned on a
             much smaller, quantum-mechanical one, improving numerical accuracy and
             stability in computational practice. We demonstrate feasibility for a
             series of equivariant graph-NN potentials for carbon, and we carry out
             initial experiments to test the limits of the
             approach.</jats:p>},
}

@Article{Liu_GeosciFront_2024_v15_p101735,
    author =   {Jie Liu and Tao Zhang and Shuyu Sun},
    title =    {{Review of deep learning algorithms in molecular simulations and
             perspective applications on petroleum engineering}},
    journal =  {Geosci. Front.},
    year =     2024,
    volume =   15,
    number =   2,
    pages =    101735,
    doi =      {10.1016/j.gsf.2023.101735},
}

@Article{Yang_Arxiv_2024_v135,
    author =   {Guang Yang and Yuan-Bin Liu and Lei Yang and Bing-Yang Cao},
    title =    {{Machine-learned atomic cluster expansion potentials for fast and
             quantum-accurate thermal simulations of wurtzite AlN}},
    journal =  {Arxiv},
    year =     2024,
    volume =   135,
    number =   8,
    doi =      {10.1063/5.0188905},
    abstract = {<jats:p>Thermal transport in wurtzite
             aluminum nitride (w-AlN) significantly affects the performance and
             reliability of corresponding electronic devices, particularly when
             lattice strains inevitably impact the thermal properties of w-AlN in
             practical applications. To accurately model the thermal properties of
             w-AlN with high efficiency, we develop a machine learning interatomic
             potential based on the atomic cluster expansion (ACE) framework. The
             predictive power of the ACE potential against density functional
             theory (DFT) is demonstrated across a broad range of properties of
             w-AlN, including ground-state lattice parameters, specific heat
             capacity, coefficients of thermal expansion, bulk modulus, and
             harmonic phonon dispersions. Validation of lattice thermal
             conductivity is further carried out by comparing the ACE-predicted
             values to the DFT calculations and experiments, exhibiting the overall
             capability of our ACE potential in sufficiently describing anharmonic
             phonon interactions. As a practical application, we perform a lattice
             dynamics analysis using the potential to unravel the effects of
             biaxial strains on thermal conductivity and phonon properties of
             w-AlN, which is identified as a significant tuning factor for near-
             junction thermal design of w-AlN-based
             electronics.</jats:p>},
}

@Article{Zhang_ComputMaterSci_2024_v235_p112843,
    author =   {Shihao Zhang and Fanshun Meng and Rong Fu and Shigenobu Ogata},
    title =    {{Highly efficient and transferable interatomic potentials for
             $\alpha$-iron and $\alpha$-iron/hydrogen binary systems using deep neural networks}},
    journal =  {Comput. Mater. Sci.},
    year =     2024,
    volume =   235,
    pages =    112843,
    doi =      {10.1016/j.commatsci.2024.112843},
}

@Article{Bertani_JChemTheoryComput_2024_v20_p1358,
    author =   {Marco Bertani and Thibault Charpentier and Francesco Faglioni and
             Alfonso Pedone},
    title =    {{Accurate and Transferable Machine Learning Potential for Molecular
             Dynamics Simulation of Sodium Silicate Glasses}},
    journal =  {J. Chem. Theory Comput.},
    year =     2024,
    volume =   20,
    number =   3,
    pages =    {1358--1370},
    doi =      {10.1021/acs.jctc.3c01115},
    abstract = {An accurate and transferable machine learning (ML) potential for the
             simulation of binary sodium silicate glasses over a wide range of
             compositions (from 0 to 50{\%} Na2O) was developed. The potential
             energy surface is approximated by the sum of atomic energy
             contributions mapped by a neural network algorithm from the local
             geometry comprising information on atomic distances and angles with
             neighboring atoms using the DeePMD code [Wang, H. Comput. Phys.
             Commun. 2018, 228, 178-184]. Our model was trained on a large data set
             of total energies and atomic forces computed at the density functional
             theory level on structures extracted from classical molecular dynamics
             (MD) simulations performed at several temperatures from 300 to 3000 K.
             This allows for the generation of a robust and transferable ML
             potential applicable over the full compositional range of glass
             formability at different temperatures that outperforms the empirical
             potentials available in the literature in reproducing structures and
             properties such as bond angle distribution, total distribution
             functions, and vibrational density of state. The generality of the
             approach enables the future training of a potential with other or more
             elements allowing for simulations of structures, properties, and
             behavior of ternary and multicomponent oxide glasses with nearly ab
             initio accuracy at a fraction of the computational cost.},
}

@Article{BinFaheem_JPhysChemC_2024_v128_p2163,
    author =   {Abdullah {\{}Bin Faheem{\}} and Kyung-Koo Lee},
    title =    {{Development of a Neural Network Potential for Modeling Molten LiCl/KCl
             Salts: Bridging Efficiency and Accuracy}},
    journal =  {J. Phys. Chem. C},
    year =     2024,
    volume =   128,
    number =   5,
    pages =    {2163--2178},
    doi =      {10.1021/acs.jpcc.3c07010},
}

@Article{Guo_AcsCatal_2024_v14_p1232,
    author =   {Jiawei Guo and Tyler Sours and Sam Holton and Chenghan Sun and
             Ambarish R. Kulkarni},
    title =    {{Screening Cu-Zeolites for Methane Activation Using Curriculum-Based
             Training}},
    journal =  {Acs Catal.},
    year =     2024,
    volume =   14,
    number =   3,
    pages =    {1232--1242},
    doi =      {10.1021/acscatal.3c05275},
    abstract = {Machine learning (ML), when used synergistically with atomistic
             simulations, has recently emerged as a powerful tool for accelerated
             catalyst discovery. However, the application of these techniques has
             been limited by the lack of interpretable and transferable ML models.
             In this work, we propose a curriculum-based training (CBT) philosophy
             to systematically develop reactive machine learning potentials (rMLPs)
             for high-throughput screening of zeolite catalysts. Our CBT approach
             combines several different types of calculations to gradually teach
             the ML model about the relevant regions of the reactive potential
             energy surface. The resulting rMLPs are accurate, transferable, and
             interpretable. We further demonstrate the effectiveness of this
             approach by exhaustively screening thousands of [CuOCu]2+ sites across
             hundreds of Cu-zeolites for the industrially relevant methane
             activation reaction. Specifically, this large-scale analysis of the
             entire International Zeolite Association (IZA) database identifies a
             set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS)
             that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed
             methane activation. We believe that this CBT philosophy can be
             generally applied to other zeolite-catalyzed reactions and,
             subsequently, to other types of heterogeneous catalysts. Thus, this
             represents an important step toward overcoming the long-standing
             barriers within the computational heterogeneous catalysis community.},
}

@Article{Gong_PhysRevB_2024_v109_p54117,
    author =   {Zhanpeng Gong and Jefferson Zhe Liu and Xiangdong Ding and Jun Sun and
             Junkai Deng},
    title =    {{Strain-induced two-dimensional relaxor ferroelectrics in Se-doped PbTe}},
    journal =  {Phys. Rev. B},
    year =     2024,
    volume =   109,
    number =   5,
    pages =    54117,
    doi =      {10.1103/PhysRevB.109.054117},
}

@Article{Zhang_PhysRevAppl_2024_v21_p24043,
    author =   {Pan Zhang and Dan Jin and Mi Qin and Zhenhua Zhang and Yong Liu and
             Ziyu Wang and Zhihong Lu and Rui Xiong and Jing Shi},
    title =    {{Effects of four-phonon interaction and vacancy defects on the thermal
             conductivity of the low-temperature phase of SnSe}},
    journal =  {Phys, Rev, Appl.},
    year =     2024,
    volume =   21,
    number =   2,
    pages =    24043,
    doi =      {10.1103/PhysRevApplied.21.024043},
}

@Article{Ding_IntJMolSci_2024_v25_p1448,
    author =   {Ye Ding and Jing Huang},
    title =    {{Implementation and Validation of an OpenMM Plugin for the Deep
             Potential Representation of Potential Energy}},
    journal =  {Int. J. Mol. Sci.},
    year =     2024,
    volume =   25,
    number =   3,
    pages =    1448,
    doi =      {10.3390/ijms25031448},
    abstract = {Machine learning potentials, particularly the deep potential (DP)
             model, have revolutionized molecular dynamics (MD) simulations,
             striking a balance between accuracy and computational efficiency. To
             facilitate the DP model's integration with the popular MD engine
             OpenMM, we have developed a versatile OpenMM plugin. This plugin
             supports a range of applications, from conventional MD simulations to
             alchemical free energy calculations and hybrid DP/MM simulations. Our
             extensive validation tests encompassed energy conservation in
             microcanonical ensemble simulations, fidelity in canonical ensemble
             generation, and the evaluation of the structural, transport, and
             thermodynamic properties of bulk water. The introduction of this
             plugin is expected to significantly expand the application scope of DP
             models within the MD simulation community, representing a major
             advancement in the field.},
}

@Article{Uporov_Intermetallics_2024_v165_p108121,
    author =   {S.A. Uporov and E.V. Sterkhov and I.A. Balyakin and V.A. Bykov and
             I.S. Sipatov and A.A. Rempel},
    title =    {{Synthesis and magnetic properties of some monotectic composites
             containing ultra-dispersed particles of YGdTbDyHo high-entropy alloy}},
    journal =  {Intermetallics},
    year =     2024,
    volume =   165,
    pages =    108121,
    doi =      {10.1016/j.intermet.2023.108121},
}

@Article{Kamath_JPhysChemA_2024_v128_p807,
    author =   {Ganesh Kamath and Alexey Illarionov and Serzhan Sakipov and Leonid
             Pereyaslavets and Igor V. Kurnikov and Oleg Butin and Ekaterina
             Voronina and Ilya Ivahnenko and Igor Leontyev and Grzegorz Nawrocki
             and Mikhail Darkhovskiy and Michael Olevanov and Yevhen K.
             Cherniavskyi and Christopher Lock and Sean Greenslade and YuChun Chen
             and Roger D. Kornberg and Michael Levitt and Boris Fain},
    title =    {{Combining Force Fields and Neural Networks for an Accurate
             Representation of Bonded Interactions}},
    journal =  {J. Phys. Chem., A},
    year =     2024,
    volume =   128,
    number =   4,
    pages =    {807--812},
    doi =      {10.1021/acs.jpca.3c07598},
    abstract = {We present a formalism of a neural network encoding bonded
             interactions in molecules. This intramolecular encoding is consistent
             with the models of intermolecular interactions previously designed by
             this group. Variants of the encoding fed into a corresponding neural
             network may be used to economically improve the representation of
             torsional degrees of freedom in any force field. We test the accuracy
             of the reproduction of the ab initio potential energy surface on a set
             of conformations of two dipeptides, methyl-capped ALA and ASP, in
             several scenarios. The encoding, either alone or in conjunction with
             an analytical potential, improves agreement with ab initio energies
             that are on par with those of other neural network-based potentials.
             Using the encoding and neural nets in tandem with an analytical model
             places the agreements firmly within {''}chemical accuracy{''} of
             {\ensuremath{\pm}}0.5 kcal/mol.},
}

@Article{Zhai_ApplPhysA_2024_v130_p106,
    author =   {B. Zhai and J. Chang and G. X. Li and H. P. Wang},
    title =    {{Grain refinement mechanism of boron addition within Ti{\textendash}Al
             alloy}},
    journal =  {Appl. Phys. A},
    year =     2024,
    volume =   130,
    number =   2,
    pages =    106,
    doi =      {10.1007/s00339-024-07277-1},
}

@Article{Guo_PhysChemChemPhys_2024_v26_p6590,
    author =   {Yuliang Guo and Xiaobo Sun and Handong Jiao and Liwen Zhang and
             Wenxuan Qin and Xiaoli Xi and Zuoren Nie},
    title =    {{Effect of electric fields on tungsten distribution in Na2WO4-WO3
             molten salt}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   8,
    pages =    {6590--6599},
    doi =      {10.1039/d3cp06202c},
    abstract = {Tungsten coatings have unique properties such as high melting points
             and hardness and are widely used in the nuclear fusion and aviation
             fields. In experiments, compared to pure Na2WO4 molten salt,
             electrolysis with Na2WO4-WO3 molten salt results in a lower deposition
             voltage. Herein, an investigation combining experimental and
             computational approaches was conducted, involving molecular dynamics
             simulations with deep learning, high-temperature in situ Raman
             spectroscopy and activation strain model analysis. The results
             indicated that the molten salt system's behaviour, influenced by
             migration and polarization effects, led to increased formation of
             Na2W2O7 in the Na2WO4-WO3 molten salt, which has a lower decomposition
             voltage and subsequently accelerated the cathodic deposition of
             tungsten. We analyzed the mechanism of the effect of the electric
             field on the Na2W2O7 structure based on the bond strength and electron
             density. This research provides crucial theoretical support for the
             effect of electric field on tungsten in molten salt and demonstrates
             the feasibility of using machine learning-based DPMD methods in
             simulating tungsten-containing molten salt systems.},
}

@Article{Yang_ChemSci_2024_v15_p3382,
    author =   {Manyi Yang and Enrico Trizio and Michele Parrinello},
    title =    {{Structure and polymerization of liquid sulfur across the
             {\ensuremath{\lambda}}-transition}},
    journal =  {Chem. Sci.},
    year =     2024,
    volume =   15,
    number =   9,
    pages =    {3382--3392},
    doi =      {10.1039/d3sc06282a},
    abstract = {The anomalous {\ensuremath{\lambda}}-transition of liquid sulfur,
             which is supposed to be related to the transformation of eight-
             membered sulfur rings into long polymeric chains, has attracted
             considerable attention. However, a detailed description of the
             underlying dynamical polymerization process is still missing. Here, we
             study the structures and the mechanism of the polymerization processes
             of liquid sulfur across the {\ensuremath{\lambda}}-transition as well
             as its reverse process of formation of the rings. We do so by
             performing ab initio-quality molecular dynamics simulations thanks to
             a combination of machine learning potentials and state-of-the-art
             enhanced sampling techniques. With our approach, we obtain structural
             results that are in good agreement with the experiments and we report
             precious dynamical insights into the mechanisms involved in the
             process.},
}

@Article{Li_ComputMaterSci_2024_v232_p112656,
    author =   {Tao Li and Qing Hou and Jie-chao Cui and Jia-hui Yang and Ben Xu and
             Min Li and Jun Wang and Bao-qin Fu},
    title =    {{Deep learning interatomic potential for thermal and defect behaviour
             of aluminum nitride with quantum accuracy}},
    journal =  {Comput. Mater. Sci.},
    year =     2024,
    volume =   232,
    pages =    112656,
    doi =      {10.1016/j.commatsci.2023.112656},
}

@Article{Ju_ComputMaterSci_2024_v232_p112664,
    author =   {Shin-Pon Ju and Chao-Chuan Huang and Hsing-Yin Chen},
    title =    {{Illuminating the mechanical responses of amorphous boron nitride
             through deep learning: A molecular dynamics study}},
    journal =  {Comput. Mater. Sci.},
    year =     2024,
    volume =   232,
    pages =    112664,
    doi =      {10.1016/j.commatsci.2023.112664},
}

@Article{Wang_PhysChemChemPhys_2024_v26_p6351,
    author =   {Xin-Xuan Wang and Ting Song and Zhen-Shuai Lei and Xiao-Wei Sun and
             Jun-Hong Tian and Zi-Jiang Liu},
    title =    {{Study of high-pressure thermophysical properties of orthocarbonate
             Sr3CO5 using deep learning molecular dynamics simulations}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   7,
    pages =    {6351--6361},
    doi =      {10.1039/d3cp04833k},
    abstract = {The exploration of the physical attributes of the recently discovered
             orthocarbonate Sr3CO5 is significant for comprehending the carbon
             cycle and storage mechanisms within the Earth's interior. In this
             study, first-principles calculations are initially used to examine the
             structural phase transitions of Sr3CO5 polymorphs within the range of
             lower mantle pressures. The results suggest that Sr3CO5 with the Cmcm
             phase exhibits a minimal enthalpy between 8.3 and 30.3 GPa. As the
             pressure exceeds 30.3 GPa, the Cmcm phase undergoes a transition to
             the I4/mcm phase, while the experimentally observed Pnma phase remains
             metastable under our studied pressure. Furthermore, the structural
             data of SrO, SrCO3, and Sr3CO5 polymorphs are utilized to develop a
             deep learning potential model suitable for the Sr-C-O system, and the
             pressure-volume relationship and elastic constants calculated using
             the potential model are in line with the available results.
             Subsequently, the elastic properties of Cmcm and I4/mcm phases in
             Sr3CO5 at high temperature and pressure are calculated using the
             molecular dynamics method. The results indicate that the I4/mcm phase
             exhibits higher temperature sensitivity in terms of elastic moduli and
             wave velocities compared to the Cmcm phase. Finally, the thermodynamic
             properties of the Cmcm and I4/mcm phases are predicted in the range of
             0-2000 K and 10-120 GPa, revealing that the heat capacity and bulk
             thermal expansion coefficient of both phases increase with
             temperature, with the constant volume heat capacity gradually
             approaching the Dulong-Petit limit as the temperature rises.},
}

@Article{Zhang_CellRepPhysSci_2024_v5_p101760,
    author =   {Junjie Zhang and Hao Zhang and Jing Wu and Xin Qian and Bai Song and
             Cheng-Te Lin and Te-Huan Liu and Ronggui Yang},
    title =    {{Vacancy-induced phonon localization in boron arsenide using a unified
             neural network interatomic potential}},
    journal =  {Cell Rep. Phys. Sci.},
    year =     2024,
    volume =   5,
    number =   1,
    pages =    101760,
    doi =      {10.1016/j.xcrp.2023.101760},
}

@Article{Fan_Nanoscale_2024_v16_p3438,
    author =   {Dong Fan and Aydin Ozcan and Pengbo Lyu and Guillaume Maurin},
    title =    {{Unravelling abnormal in-plane stretchability of two-dimensional metal-
             organic frameworks by machine learning potential molecular dynamics}},
    journal =  {Nanoscale},
    year =     2024,
    volume =   16,
    number =   7,
    pages =    {3438--3447},
    doi =      {10.1039/d3nr05966a},
    abstract = {Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense
             potential for various applications due to their distinctive intrinsic
             properties compared to their 3D analogues. Herein, we designed a
             highly stable NiF2(pyrazine)2 2D MOF in silico with a two-dimensional
             periodic wine-rack architecture. Extensive first-principles
             calculations and molecular dynamics (MD) simulations based on a newly
             developed machine learning potential (MLP) revealed that this 2D MOF
             exhibits huge in-plane Poisson's ratio anisotropy. This results in
             anomalous negative in-plane stretchability, as evidenced by an
             uncommon decrease in its in-plane area upon the application of
             uniaxial tensile strain, which makes this 2D MOF particularly
             attractive for flexible wearable electronics and ultra-thin sensor
             applications. We further demonstrated the unique capability of MLP to
             accurately predict the finite-temperature properties of MOFs on a
             large scale, exemplified by MLP-MD simulations with a dimension of
             28.2 {\texttimes} 28.2 nm2, relevant to the length scale
             experimentally attainable for the fabrication of MOF films.},
}

@Article{Feng_JMolLiq_2024_v394_p123533,
    author =   {Taixi Feng and Guimin Lu},
    title =    {{Hydration MgCl2-NaCl-KCl molten salt using a novel approach for
             training machine learning potential}},
    journal =  {J. Mol. Liq.},
    year =     2024,
    volume =   394,
    pages =    123533,
    doi =      {10.1016/j.molliq.2023.123533},
}

@Article{Thakur_JChemPhys_2024_v160_p024502,
    author =   {Atul C. Thakur and Richard C. Remsing},
    title =    {{Nuclear quantum effects in the acetylene:ammonia plastic co-crystal}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   2,
    pages =    024502,
    doi =      {10.1063/5.0179161},
    abstract = {Organic molecular solids can exhibit rich phase diagrams. In addition
             to structurally unique phases, translational and rotational degrees of
             freedom can melt at different state points, giving rise to partially
             disordered solid phases. The structural and dynamic disorder in these
             materials can have a significant impact on the physical properties of
             the organic solid, necessitating a thorough understanding of disorder
             at the atomic scale. When these disordered phases form at low
             temperatures, especially in crystals with light nuclei, the prediction
             of material properties can be complicated by the importance of nuclear
             quantum effects. As an example, we investigate nuclear quantum effects
             on the structure and dynamics of the orientationally disordered,
             translationally ordered plastic phase of the acetylene:ammonia (1:1)
             co-crystal that is expected to exist on the surface of Saturn's moon
             Titan. Titan's low surface temperature ({\ensuremath{\sim}}90{~}K)
             suggests that the quantum mechanical behavior of nuclei may be
             important in this and other molecular solids in these environments. By
             using neural network potentials combined with ring polymer molecular
             dynamics simulations, we show that nuclear quantum effects increase
             orientational disorder and rotational dynamics within the
             acetylene:ammonia (1:1) co-crystal by weakening hydrogen bonds. Our
             results suggest that nuclear quantum effects are important to
             accurately model molecular solids and their physical properties in
             low-temperature environments.},
}

@Article{Kussainova_JChemEngData_2024_v69_p204,
    author =   {Dina Kussainova and Athanassios Z. Panagiotopoulos},
    title =    {{Molecular Simulation of Lithium Carbonate Reactive
             Vapor{\textendash}Liquid Equilibria Using a Deep Potential Model}},
    journal =  {J. Chem. Eng. Data},
    year =     2024,
    volume =   69,
    number =   1,
    pages =    {204--214},
    doi =      {10.1021/acs.jced.3c00580},
}

@Article{Dong_AcsApplMaterInterfaces_2024_v16_p530,
    author =   {Wenhao Dong and Heqing Tian and Wenguang Zhang and Jun-Jie Zhou and
             Xinchang Pang},
    title =    {{Development of NaCl-MgCl2-CaCl2 Ternary Salt for High-Temperature
             Thermal Energy Storage Using Machine Learning}},
    journal =  {Acs Appl. Mater. Interfaces},
    year =     2024,
    volume =   16,
    number =   1,
    pages =    {530--539},
    doi =      {10.1021/acsami.3c13412},
    abstract = {NaCl-MgCl2-CaCl2 eutectic ternary chloride salts are potential heat
             transfer and storage materials for high-temperature thermal energy
             storage. In this study, first-principles molecular dynamics simulation
             results were used as a data set to develop an interatomic potential
             for ternary chloride salts using a neural network machine learning
             method. Deep potential molecular dynamics (DPMD) simulations were
             performed to predict the microstructure and thermophysical properties
             of the NaCl-MgCl2-CaCl2 ternary salt. This work reveals that DPMD
             simulations can accurately calculate the microstructure and
             thermophysical properties of ternary chloride salts. The association
             strength of chloride ions and cations follows the order of Mg2+
             > Ca2+ > Na+, and the coordination
             number decreases gradually with increasing temperature, indicating a
             progressively looser and more disordered molten structure.
             Furthermore, thermophysical properties, such as density, specific heat
             capacity, thermal conductivity, and viscosity, are in good agreement
             with the experimental measurements. Machine learning molecular
             dynamics will provide a feasible multivariate molten salt exploration
             method for the design of next-generation solar power plants and
             thermal energy storage systems.},
}

@Article{Hedelius_JChemTheoryComput_2024_v20_p199,
    author =   {Bryce E. Hedelius and Damon Tingey and Dennis {\{}Della Corte{\}}},
    title =    {{TrIP-Transformer Interatomic Potential Predicts Realistic Energy
             Surface Using Physical Bias}},
    journal =  {J. Chem. Theory Comput.},
    year =     2024,
    volume =   20,
    number =   1,
    pages =    {199--211},
    doi =      {10.1021/acs.jctc.3c00936},
    abstract = {Accurate interatomic energies and forces enable high-quality molecular
             dynamics simulations, torsion scans, potential energy surface
             mappings, and geometry optimizations. Machine learning algorithms have
             enabled rapid estimates of the energies and forces with high accuracy.
             Further development of machine learning algorithms holds promise for
             producing universal potentials that support many different atomic
             species. We present the Transformer Interatomic Potential (TrIP): a
             chemically sound potential based on the SE(3)-Transformer. TrIP's
             species-agnostic architecture, which uses continuous atomic
             representation and homogeneous graph convolutions, encourages
             parameter sharing between atomic species for more general
             representations of chemical environments, maintains a reasonable
             number of parameters, serves as a form of regularization, and is a
             step toward accurate universal interatomic potentials. TrIP achieves
             state-of-the-art accuracies on the COMP6 benchmark with an energy
             prediction of just 1.02 kcal/mol MAE. We introduce physical bias in
             the form of Ziegler-Biersack-Littmark-screened nuclear repulsion and
             constrained atomization energies. An energy scan of a water molecule
             demonstrates that these changes improve long- and near-range
             interactions compared to other neural network potentials. TrIP also
             demonstrates stability in molecular dynamics simulations,
             demonstrating reasonable exploration of Ramachandran space.},
}

@Article{Kwon_AcsApplMaterInterfaces_2024_v16_p31687,
    author =   {Hyuna Kwon and Marcos F. {\{}Calegari Andrade{\}} and Shane Ardo and
             Daniel V. Esposito and Tuan Anh Pham and Tadashi Ogitsu},
    title =    {{Confinement Effects on Proton Transfer in TiO2 Nanopores from Machine
             Learning Potential Molecular Dynamics Simulations}},
    journal =  {Acs Appl. Mater. Interfaces},
    year =     2024,
    volume =   16,
    number =   24,
    pages =    {31687--31695},
    doi =      {10.1021/acsami.4c02339},
    abstract = {Improved understanding of proton transfer in nanopores is critical for
             a wide range of emerging applications, yet experimentally probing
             mechanisms and energetics of this process remains a significant
             challenge. To help reveal details of this process, we developed and
             applied a machine learning potential derived from first-principles
             calculations to examine water reactivity and proton transfer in TiO2
             slit-pores. We find that confinement of water within pores smaller
             than 0.5 nm imposes strong and complex effects on water reactivity and
             proton transfer. Although the proton transfer mechanism is similar to
             that at a TiO2 interface with bulk water, confinement reduces the
             activation energy of this process, leading to more frequent proton
             transfer events. This enhanced proton transfer stems from the
             contraction of oxygen-oxygen distances dictated by the interplay
             between confinement and hydrophilic interactions. Our simulations also
             highlight the importance of the surface topology, where faster proton
             transport is found in the direction where a unique arrangement of
             surface oxygens enables the formation of an ordered water chain. In a
             broader context, our study demonstrates that proton transfer in
             hydrophilic nanopores can be enhanced by controlling pore size,
             surface chemistry, and topology.},
}

@Article{Raman_MolPhys_2024,
    author =   {Abhinav S. Raman and Annabella Selloni},
    title =    {{An             <i>ab-
             initio</i>             deep neural
             network potential for accurate large-scale simulations of the
             muscovite mica-water interface}},
    journal =  {Mol. Phys.},
    year =     2024,
    doi =      {10.1080/00268976.2024.2365430},
}

@Article{Urata_JAmCeramSoc_2024,
    author =   {Shingo Urata and Marco Bertani and Alfonso Pedone},
    title =    {{Applications of machine{-}learning interatomic potentials for modeling
             ceramics, glass, and electrolytes: A review}},
    journal =  {J Am Ceram Soc.},
    year =     2024,
    doi =      {10.1111/jace.19934},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>The
             emergence of artificial intelligence has provided efficient
             methodologies to pursue innovative findings in material science. Over
             the past two decades, machine{-}learning potential (MLP) has emerged
             as an alternative technology to density functional theory (DFT) and
             classical molecular dynamics (CMD) simulations for computational
             modeling of materials and estimation of their properties. The MLP
             offers more efficient computation compared to DFT, while providing
             higher accuracy compared to CMD. This enables us to conduct more
             realistic simulations using models with more atoms and for longer
             simulation times. Indeed, the number of research studies utilizing
             MLPs has significantly increased since 2015, covering a broad range of
             materials and their structures, ranging from simple to complex, as
             well as various chemical and physical phenomena. As a result, there
             are high expectations for further applications of MLPs in the field of
             material science and industrial development. This review aims to
             summarize the applications, particularly in ceramics and glass
             science, and fundamental theories of MLPs to facilitate future
             progress and utilization. Finally, we provide a summary and discuss
             perspectives on the next challenges in the development and application
             of{~}MLPs.</jats:p>},
}

@Article{Chen_JChemTheoryComput_2024_v20_p4703,
    author =   {Yuzhuo Chen and Sebastian V. Pios and Maxim F. Gelin and Lipeng Chen},
    title =    {{Accelerating Molecular Vibrational Spectra Simulations with a
             Physically Informed Deep Learning Model}},
    journal =  {J. Chem. Theory Comput.},
    year =     2024,
    volume =   20,
    number =   11,
    pages =    {4703--4710},
    doi =      {10.1021/acs.jctc.4c00173},
    abstract = {In recent years, machine learning (ML) surrogate models have emerged
             as an indispensable tool to accelerate simulations of physical and
             chemical processes. However, there is still a lack of ML models that
             can accurately predict molecular vibrational spectra. Here, we present
             a highly efficient multitask ML surrogate model termed Vibrational
             Spectra Neural Network (VSpecNN), to accurately calculate infrared
             (IR) and Raman spectra based on dipole moments and polarizabilities
             obtained on-the-fly via ML-enhanced molecular dynamics simulations.
             The methodology is applied to pyrazine, a prototypical polyatomic
             chromophore. The VSpecNN-predicted energies are well within the
             chemical accuracy (1 kcal/mol), and the errors for VSpecNN-predicted
             forces are only half of those obtained from a popular high-performance
             ML model. Compared to the ab initio reference, the VSpecNN-predicted
             frequencies of IR and Raman spectra differ only by less than 5.87
             cm-1, and the intensities of IR spectra and the depolarization ratios
             of Raman spectra are well reproduced. The VSpecNN model developed in
             this work highlights the importance of constructing highly accurate
             neural network potentials for predicting molecular vibrational
             spectra.},
}

@Article{Du_ChemMater_2024_v36_p6167,
    author =   {Tao Du and Xuan Ge and Fengming Cao and Han Liu and Caijuan Shi and
             Junwei Ding and Daming Sun and Qiangqiang Zhang and Yuanzheng Yue and
             Morten M. Smedskjaer},
    title =    {{Structural Origin of the Deformation Propensity of Zeolitic
             Imidazolate Framework Glasses}},
    journal =  {Chem. Mater.},
    year =     2024,
    volume =   36,
    number =   12,
    pages =    {6167--6179},
    doi =      {10.1021/acs.chemmater.4c00921},
}

@Article{Bhullar_ChemphyschemEurJChemPhysPhysChem_2024_pe202400090,
    author =   {Mangladeep Bhullar and Zihao Bai and Akinwumi Akinpelu and Yansun Yao},
    title =    {{Phase Transition in Silicon from Machine Learning Informed
             Metadynamics}},
    journal =  {Chemphyschem: Eur. J. Chem. Phys. Phys. Chem.},
    year =     2024,
    pages =    {e202400090},
    doi =      {10.1002/cphc.202400090},
    abstract = {Investigating reconstructive phase transitions in large-sized systems
             requires a highly efficient computational framework with computational
             cost proportional to the system size. Traditionally, widely used
             frameworks such as density functional theory (DFT) have been
             prohibitively expensive for extensive simulations on large systems
             that require long-time scales. To address this challenge, this study
             employed well-trained machine learning potential to simulate phase
             transitions in a large-size system. This work integrates the
             metadynamics simulation approach with machine learning potential,
             specifically deep potential, to enhance computational efficiency and
             accelerate the study of phase transition and consequent development of
             grains and dislocation defects in a system. The new method is
             demonstrated using the phase transitions of bulk silicon under high
             pressure. This approach has revealed the transition path and formation
             of polycrystalline silicon systems under specific stress conditions,
             demonstrating the effectiveness of deep potential-driven metadynamics
             simulations in gaining insights into complex material behaviors in
             large-sized systems.},
}

@Article{Zhang_CeramInt_2024_v50_p30008,
    author =   {Meng Zhang and Siyan Deng and Jianghong Zhang and Daxin Li and Dechang
             Jia and Yu Zhou},
    title =    {{The structural evolution of SiBCNZr amorphous ceramics analyzed by
             machine learning potential}},
    journal =  {Ceram. Int.},
    year =     2024,
    volume =   50,
    number =   17,
    pages =    {30008--30017},
    doi =      {10.1016/j.ceramint.2024.05.296},
}

@Article{Gong_AngewChemInternationalEngl_2024_v63_pe202405379,
    author =   {Fu-Qiang Gong and Yun-Pei Liu and Ye Wang and Weinan E and Zhong-Qun
             Tian and Jun Cheng},
    title =    {{Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on
             Catalysis through Surface Pre-melting of Nanoclusters}},
    journal =  {Angew. Chem. (International, Engl.)},
    year =     2024,
    volume =   63,
    number =   27,
    pages =    {e202405379},
    doi =      {10.1002/anie.202405379},
    abstract = {Due to the superior catalytic activity and efficient utilization of
             noble metals, nanocatalysts are extensively used in the modern
             industrial production of chemicals. The surface structures of these
             materials are significantly influenced by reactive adsorbates, leading
             to dynamic behavior under experimental conditions. The dynamic nature
             poses significant challenges in studying the structure-activity
             relations of catalysts. Herein, we unveil an anomalous entropic effect
             on catalysis via surface pre-melting of nanoclusters through machine
             learning accelerated molecular dynamics and free energy calculation.
             We find that due to the pre-melting of shell atoms, there exists a
             non-linear variation in the catalytic activity of the nanoclusters
             with temperature. Consequently, two notable changes in catalyst
             activity occur at the respective temperatures of melting for the shell
             and core atoms. We further study the nanoclusters with surface point
             defects, i.e. vacancy and ad-atom, and observe significant decrease in
             the surface melting temperatures of the nanoclusters, enabling the
             reaction to take place under more favorable and milder conditions.
             These findings not only provide novel insights into dynamic catalysis
             of nanoclusters but also offer new understanding of the role of point
             defects in catalytic processes.},
}

@Article{Feng_BrazJChemEng_2024,
    author =   {Taixi Feng and Zhaoting Liu and Guimin Lu},
    title =    {{Deep potential molecular dynamic and electrochemical experiments to
             reveal the structure and behavior of Mn(II) in magnesium electrolysis}},
    journal =  {Braz. J. Chem. Eng.},
    year =     2024,
    doi =      {10.1007/s43153-024-00465-9},
}

@Article{Wang_AdvFunctMater_2024,
    author =   {Lei Wang and Zehua Gao and Kerong Su and Nhat Truong Nguyen and
             Rui{-}Ting Gao and Junxiang Chen and Lei Wang},
    title =    {{Stacked High{-}Entropy Hydroxides Promote Charge Transfer Kinetics for
             Photoelectrochemical Water Splitting}},
    journal =  {Adv Funct Mater.},
    year =     2024,
    doi =      {10.1002/adfm.202403948},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>Although
             various kinds of cocatalyst are developed and decorated on the bismuth
             vanadate (BiVO<jats:sub>4{\ensuremath{<}
             }/jats:sub>) photoanode, its photoelectrochemical (PEC)
             water splitting performance is limited owing to severe charge
             recombination and sluggish oxygen evolution reaction (OER). Herein, a
             high{-}entropy hydroxide electrocatalyst (FeCoNiMoCrOOH) is
             constructed as a co{-}catalyst deposited on BiVO<jats:s
             ub>4</jats:sub> with a
             good PEC activity and stability in potassium borate buffer, addressing
             substantial charge recombination and poor surface oxygen evolution
             reaction of the material. FeCoNiMoCrOOH synthesized by a simple
             electrodeposition stacking strategy, delivers an overpotential of
             172{~}mV at 10{~}mA{~}cm<jats:sup>{\ensu
             remath{-}}2</jats:sup> with a stability
             of 600{~}h under alkaline conditions, representing one of the best
             performances on high{-}entropy{-}based catalysts. The FeCoNiMoCrOOH/Bi
             VO<jats:sub>4</jats:sub{\
             ensuremath{>}} photoanode shows a photocurrent density of 5.23{~}mA{~}
             cm<jats:sup>{\ensuremath{-
             }}2</jats:sup> at 1.23 V<
             jats:sub>RHE</jats:sub>
             with 100{~}h durability in potassium borate buffer. Experimental
             investigations and theoretical calculations demonstrate that the
             synergistic effect of Mo and Cr in FeCoNi catalyst effectively
             decreases the dissolution Fe, Co, and Ni after long{-}term operation,
             increases the charge transfer kinetics, and promotes OER and PEC
             performances, therefore enhancing the photocorrosion resistance of BiV
             O<jats:sub>4</jats:sub{\e
             nsuremath{>}}. This work provides a new avenue to design high
             entropy{-}based electrocatalysts boosting solar water splitting
             activity and stability.</jats:p>},
}

@Article{Fazel_JComputChem_2024_v45_p1821,
    author =   {Kamron Fazel and Nima Karimitari and Tanooj Shah and Christopher
             Sutton and Ravishankar Sundararaman},
    title =    {{Improving the reliability of machine learned potentials for modeling
             inhomogeneous liquids}},
    journal =  {J. Comput. Chem.},
    year =     2024,
    volume =   45,
    number =   21,
    pages =    {1821--1828},
    doi =      {10.1002/jcc.27353},
    abstract = {The atomic-scale response of inhomogeneous fluids at interfaces and
             surrounding solute particles plays a critical role in governing
             chemical, electrochemical, and biological processes. Classical
             molecular dynamics simulations have been applied extensively to
             simulate the response of fluids to inhomogeneities directly, but are
             limited by the accuracy of the underlying interatomic potentials.
             Here, we use neural network potentials (NNPs) trained to ab initio
             simulations to accurately predict the inhomogeneous responses of two
             distinct fluids: liquid water and molten NaCl. Although NNPs can be
             readily trained to model complex bulk systems across a range of state
             points, we show that to appropriately model a fluid's response at an
             interface, relevant inhomogeneous configurations must be included in
             the training data. In order to sufficiently sample appropriate
             configurations of such inhomogeneous fluids, we develop protocols
             based on molecular dynamics simulations in the presence of external
             potentials. We demonstrate that NNPs trained on inhomogeneous fluid
             configurations can more accurately predict several key properties of
             fluids-including the density response, surface tension and size-
             dependent cavitation free energies-for liquid water and molten NaCl,
             compared to both empirical interatomic potentials and NNPs that are
             not trained on such inhomogeneous configurations. This work therefore
             provides a first demonstration and framework to extract the response
             of inhomogeneous fluids from first principles for classical density-
             functional treatment of fluids free from empirical potentials.},
}

@Article{Yang_AdvEnergyMater_2024,
    author =   {Xiaochen Yang and Sunny Gupta and Yu Chen and Dogancan Sari and
             Han{-}Ming Hau and Zijian Cai and Chaochao Dun and Miao Qi and Lu Ma
             and Yi Liu and Jeffrey J. Urban and Gerbrand Ceder},
    title =    {{Fast Room{-}Temperature Mg{-}Ion Conduction in Clay{-}Like Halide
             Glassy Electrolytes}},
    journal =  {Adv. Energy Mater.},
    year =     2024,
    doi =      {10.1002/aenm.202400163},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>The
             discovery of mechanically soft solid{-}state materials with fast
             Mg{-}ion conduction is crucial for the development of solid{-}state
             magnesium batteries. In this paper, novel magnesium gallium halide
             compounds are reported that achieve high ionic conductivity of 0.47 mS
             {~}cm<jats:sup>{\ensuremath{-
             }}1</jats:sup> at room temperature.
             These Mg{-}ion conductors obtained by ball milling Mg and Ga salts
             exhibit clay{-}like mechanical properties, enabling intimate contact
             at the electrode{\textendash}electrolyte interface during battery
             cycling. With a combination of experimental and computational
             analysis, this study identifies that the soft{-}clay formation is
             induced by partial anion exchange during milling. This partial anion
             exchange creates undercoordinated magnesium ions in a chlorine{-}rich
             environment, yielding fast Mg{-}ion conduction. This work demonstrates
             the potential of clay{-}like halide electrolytes for
             all{-}solid{-}state magnesium batteries, with possible further
             extension to other multivalent battery
             systems.</jats:p>},
}

@Article{XU_SciSinTech_2024_v54_p477,
    author =   {ShanSen XU and Jian CHANG and Bin ZHAI and PengXu YAN and MaoJie LIN
             and BingBo WEI},
    title =    {{Amorphous solidification mechanism of multicomponent Ti-Cu based alloy
             investigated by molecular dynamics simulation and drop tube
             experiments}},
    journal =  {Sci. Sin.-Tech.},
    year =     2024,
    volume =   54,
    number =   3,
    pages =    {477--489},
    doi =      {10.1360/SST-2023-0408},
}

@Article{Hu_AdvFunctMater_2024,
    author =   {Taiping Hu and Linhan Xu and Fuzhi Dai and Guobing Zhou and Fangjia Fu
             and Xiaoxu Wang and Linsen Li and Xinping Ai and Shenzhen Xu},
    title =    {{Impact of Amorphous LiF Coating Layers on Cathode{-}Electrolyte
             Interfaces in Solid{-}State Batteries}},
    journal =  {Adv Funct Mater.},
    year =     2024,
    doi =      {10.1002/adfm.202402993},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>High
             interfacial resistance between electrodes and solid{-}state
             electrolyte is the major cause for the failure of all{-}solid{-}state
             Li{-}ion batteries. Spontaneous (electro)chemical reactions and poor
             Li{-}ion diffusion at the interfaces are closely related to this
             increased impedance. Although introducing a coating layer can mitigate
             interfacial reactions and structural reconstruction, it may also lead
             to poor Li{-}ion diffusion. Balancing this trade{-}off therefore is
             crucial for the design of coating layer materials. In this study, the
             impact of the amorphous LiF (a{-}LiF) coating layer on interfacial
             structural reconstruction and Li{-}ion diffusion at the LiCoO{\ensurem
             ath{<}}jats:sub>2</jats:sub{\ensuremath{
             >}}/Li<jats:sub>6</jats:s
             ub>PS<jats:sub>5{\ensurem
             ath{<}}/jats:sub>Cl solid{-}state interface is
             explicitly elucidated{~}via machine{-}learning{-}assisted molecular
             dynamics (MD) simulations. It is found that the a{-}LiF can
             effectively protect the P{-}S tetrahedron local structures in Li{\ensu
             remath{<}}jats:sub>6</jats:sub{\ensurema
             th{>}}PS<jats:sub>5</jats
             :sub>Cl but cannot suppress the formation of side
             product S<jats:sub>2</jat
             s:sub> dimers. It is further discovered that once the
             a{-}LiF coating exceeds a certain critical thickness, emergence of
             ordered local structures will inhibit Li{-}ion diffusion. The
             simulations propose that the optimal thickness of the coating layer is
             around 1{~}nm. Overall, the work provides a microscopic understanding
             for effects of the a{-}LiF coating layer on the structural and kinetic
             properties of cathode{-}solid electrolyte interfaces and can guide the
             design of interfacial coating materials for solid{-}state
             batteries.</jats:p>},
}

@Article{Pikalova_DoklPhysChem_2024_v514_p9,
    author =   {N. S. Pikalova and I. A. Balyakin and A. A. Yuryev and A. A. Rempel},
    title =    {{Prediction of Mechanical Properties of High-Entropy Carbide
             (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning
             Potential}},
    journal =  {Dokl Phys Chem},
    year =     2024,
    volume =   514,
    number =   1,
    pages =    {9--14},
    doi =      {10.1134/S0012501624600049},
}

@Article{Luo_AdvEngMater_2024,
    author =   {Kun Luo and Xiao Han and Jonathan Cappola and Dian Li and Yufeng Zheng
             and Lin Li and Feng Yan and Qi An},
    title =    {{Hyper{-}Elastic Deformation via Martensitic Phase Transformation in
             Cadmium Telluride}},
    journal =  {Adv Eng Mater},
    year =     2024,
    doi =      {10.1002/adem.202302076},
    abstract = {<jats:p>Cadmium telluride (CdTe) is a
             highly promising material for photovoltaics (PV) and photodetectors
             due to its light{-}absorbing properties. However, efficient design and
             use of flexible devices require a deep understanding of its
             atomic{-}level deformation mechanism. Herein, uniaxial compression
             deformation of CdTe monocrystalline with varying crystal orientations
             is investigated using molecular dynamics (MD) with a newly developed
             machine{-}learning force field (ML{-}FF), alongside in{-}situ
             micropillar compression experiments. The findings reveal that CdTe
             bulk deformation is dominated by reversible martensitic phase
             transformation, whereas CdTe pillar deformation is primarily driven by
             dislocation nucleation and movement. CdTe monocrystals possess
             exceptional super{-}recoverable deformation along the
             {\&}lt;100{\&}gt; orientation due to hyper{-}elastic processes induced
             by martensitic transformation. This discovery not only sheds light on
             the peculiarities observed in micropillar experimental measurements,
             but also provides pivotal insights into the fundamental deformation
             behaviors of CdTe and similar II{\textendash}VI compounds under
             various stress conditions. These insights are crucial for the
             innovative design and enhanced functionality of future flexible
             electronic devices.</jats:p>},
}

@Article{Luo_PhysRevRes_2024_v6_p13292,
    author =   {Chenxing Luo and Yang Sun and Renata M. Wentzcovitch},
    title =    {{Probing the state of hydrogen in  $\delta$-AlOOH 
             at mantle conditions with machine
             learning potential}},
    journal =  {Phys, Rev, Res.},
    year =     2024,
    volume =   6,
    number =   1,
    pages =    13292,
    doi =      {10.1103/PhysRevResearch.6.013292},
    abstract = {Hydrous and nominally anhydrous minerals (NAMs) are a fundamental
             class of solids of enormous significance to geophysics. They are the
             water carriers in the deep geological water cycle and impact
             structural, elastic, plastic, and thermodynamic properties and phase
             relations in Earth's forming aggregates (rocks). They play a critical
             role in the geochemical and geophysical processes that shape the
             planet. Their complexity has prevented predictive calculations of
             their properties, but progress in materials simulations ushered by
             machine learning potentials is transforming this state of affairs.
             Here, we adopt a hybrid approach that combines deep learning
             potentials (DP) with the SCAN meta-GGA functional to simulate a
             prototypical hydrous system. We illustrate the success of this
             approach to simulate {\$}{\textbackslash}delta{\$}-AlOOH
             ({\$}{\textbackslash}delta{\$}), a phase capable of transporting water
             down to near the core-mantle boundary of the Earth
             ({\textasciitilde}2,900 km depth and {\textasciitilde}135 GPa) in
             subducting slabs. A high-throughput sampling of phase space using
             molecular dynamics simulations with DP-potentials sheds light on the
             hydrogen-bond behavior and proton diffusion at geophysical conditions.
             These simulations provide a pathway for a deeper understanding of
             these crucial components that shape Earth's internal state.},
}

@Article{Chen_SmallWeinheimBergstrGer_2024_pe2400083,
    author =   {Weiqi Chen and Kang Wang and Xinran Miao and Jie Zhang and Aisheng
             Song and Xinchun Chen and Jianbin Luo and Tianbao Ma},
    title =    {{Ultralow-Friction at Cryogenic Temperature Induced by Hydrogen
             Correlated Quantum Effect}},
    journal =  {Small (Weinheim Bergstr. Ger.)},
    year =     2024,
    pages =    {e2400083},
    doi =      {10.1002/smll.202400083},
    abstract = {Temperature is one of the governing factors affecting friction of
             solids. Undesired high friction state has been generally reported at
             cryogenic temperatures due to the prohibition of thermally activated
             processes, following conventional Arrhenius equation. This has brought
             huge difficulties to lubrication at extremely low temperatures in
             industry. Here, the study uncovers a hydrogen-correlated sub-Arrhenius
             friction behavior in hydrogenated amorphous carbon (a-C:H) film at
             cryogenic temperatures, and a stable ultralow-friction over a wide
             temperature range (103-348 K) is achieved. This is attributed to
             hydrogen-transfer-induced mild structural ordering transformation,
             confirmed by machine-learning-based molecular dynamics simulations.
             The anomalous sub-Arrhenius temperature dependence of structural
             ordering transformation rate is well-described by a quantum mechanical
             tunneling (QMT) modified Arrhenius model, which is correlated with
             quantum delocalization of hydrogen in tribochemical reactions. This
             work reveals a hydrogen-correlated friction mechanism overcoming the
             Arrhenius temperature dependence and provides a new pathway for
             achieving ultralow friction under cryogenic conditions.},
}

@Article{Pegolo_FrontMater_2024_v11,
    author =   {Paolo Pegolo and Federico Grasselli},
    title =    {{Thermal transport of glasses via machine learning driven simulations}},
    journal =  {Front., Mater,},
    year =     2024,
    volume =   11,
    doi =      {10.3389/fmats.2024.1369034},
    abstract = {<jats:p>Accessing the thermal transport
             properties of glasses is a major issue for the design of production
             strategies of glass industry, as well as for the plethora of
             applications and devices where glasses are employed. From the
             computational standpoint, the chemical and morphological complexity of
             glasses calls for atomistic simulations where the interatomic
             potentials are able to capture the variety of local environments,
             composition, and (dis)order that typically characterize glassy phases.
             Machine-learning potentials (MLPs) are emerging as a valid alternative
             to computationally expensive
             <jats:italic>ab
             initio</jats:italic> simulations,
             inevitably run on very small samples which cannot account for disorder
             at different scales, as well as to empirical force fields, fast but
             often reliable only in a narrow portion of the thermodynamic and
             composition phase diagrams. In this article, we make the point on the
             use of MLPs to compute the thermal conductivity of glasses, through a
             review of recent theoretical and computational tools and a series of
             numerical applications on vitreous silica and vitreous silicon, both
             pure and intercalated with
             lithium.</jats:p>},
}

@Article{Wen_PhysChemChemPhys_2024_v26_p9984,
    author =   {Mingjie Wen and Xiaoya Chang and Yabei Xu and Dongping Chen and
             Qingzhao Chu},
    title =    {{Determining the mechanical and decomposition properties of high
             energetic materials ({\ensuremath{\alpha}}-RDX,
             {\ensuremath{\beta}}-HMX, and {\ensuremath{\varepsilon}}-CL-20) using
             a neural network potential}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   13,
    pages =    {9984--9997},
    doi =      {10.1039/d4cp00017j},
    abstract = {Molecular simulations of high energetic materials (HEMs) are limited
             by efficiency and accuracy. Recently, neural network potential (NNP)
             models have achieved molecular simulations of millions of atoms while
             maintaining the accuracy of density functional theory (DFT) levels.
             Herein, an NNP model covering typical HEMs containing C, H, N, and O
             elements is developed. The mechanical and decomposition properties of
             1,3,5-trinitroperhydro-1,3,5-triazine (RDX),
             hexahydro-1,3,5-trinitro-1,3,5-triazine (HMX), and
             2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) are determined by
             employing the molecular dynamics (MD) simulations based on the NNP
             model. The calculated results show that the mechanical properties of
             {\ensuremath{\alpha}}-RDX, {\ensuremath{\beta}}-HMX, and
             {\ensuremath{\varepsilon}}-CL-20 agree with previous experiments and
             theoretical results, including cell parameters, equations of state,
             and elastic constants. In the thermal decomposition simulations, it is
             also found that the initial decomposition reactions of the three
             crystals are N-NO2 homolysis, corresponding radical intermediates
             formation, and NO2-induced reactions. This decomposition trajectory is
             mainly divided into two stages separating from the peak of NO2:
             pyrolysis and oxidation. Overall, the NNP model for C/H/N/O elements
             in this work is an alternative reactive force field for RDX, HMX, and
             CL-20 HEMs, and it opens up new potential for future kinetic study of
             nitramine explosives.},
}

@Article{BinJassar_AdvFunctMater_2024,
    author =   {Mohammed {\{}Bin Jassar{\}} and Carine Michel and Sara Abada and
             Theodorus {\{}De Bruin{\}} and Sylvain Tant and Carlos Nieto{-}Draghi
             and Stephan N. Steinmann},
    title =    {{A Perspective on the Molecular Modeling of Electrolyte Decomposition
             Reactions for Solid Electrolyte Interphase Growth in Lithium{-}Ion
             Batteries}},
    journal =  {Adv Funct Mater.},
    year =     2024,
    doi =      {10.1002/adfm.202313188},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>The solid
             electrolyte interphase (SEI) is a thin heterogeneous layer formed at
             the anode/electrolyte interface in lithium{-}ion batteries as a
             consequence of the reduction of the electrolyte. The initial formation
             of the SEI inhibits the direct contact between the electrode and the
             electrolyte and thus protects the battery. However, the composition,
             structure, and size of the SEI evolve over time and the growth of the
             SEI is considered the primary mechanism leading to the gradual
             deterioration of the battery performance. Despite the importance of
             the SEI and its growth, the atomistic understanding of the underlying
             elementary reaction steps remains partial. Molecular modeling of the
             electrolyte decomposition is key to gain detailed insights that are
             complementary to experiments for the reactions occurring in this
             heterogenous interphase. In this perspective, the electron transport
             mechanisms are first described from the anode to the electrolyte
             through the SEI and highlight the importance of the inorganic/organic
             interface within the heterogeneous SEI: it is where the electrolyte
             decomposition reactions are likely to occur. Finally, a view is
             provided on the current progress on molecular modeling techniques
             (e.g., Density Functional Theory, force fields, machine learning
             potentials) of the SEI and the challenges each method
             faces.</jats:p>},
}

@Article{Xu_AdvFunctMater_2024_v34,
    author =   {Bo Xu and Zhanpeng Gong and Jingran Liu and Yunfei Hong and Yang Yang
             and Lou Li and Yilun Liu and Junkai Deng and Jefferson Zhe Liu},
    title =    {{Tunable Ferroelectric Topological Defects on 2D Topological Surfaces:
             Complex Strain Engineering Skyrmion{-}Like Polar Structures in 2D
             Materials}},
    journal =  {Adv Funct Mater.},
    year =     2024,
    volume =   34,
    number =   26,
    doi =      {10.1002/adfm.202311599},
    abstract = {<jats:title>Abstract</jat
             s:title><jats:p>Polar
             topological structures in ferroelectric materials have attracted
             significant interest due to their fascinating physical properties and
             promising applications in high{-}density, nonvolatile memories.
             Currently, most polar topological patterns are only observed in the
             bulky perovskite superlattices. In this work, a discovery of tunable
             ferroelectric polar topological structures is reported, designed, and
             achieved using topological strain engineering in two{-}dimensional
             (2D) PbX (X = S, Se, and Te) materials via integrating
             first{-}principles calculations, machine learning molecular dynamics
             simulations, and continuum modeling.{~}First{-}principles calculations
             discover the strain{-}induced reversible ferroelectric phase
             transition with diverse polarization directions strongly correlated to
             the straining conditions. Taking advantage of the mechanical
             flexibility of 2D PbX, using molecular dynamics (MD) simulations, it
             is successfully demonstrated that the complex strain fields of 2D
             topological surfaces under mechanical indentation can generate unique
             skyrmion{-}like polar topological vortex patterns. Further continuum
             simulations for experimentally accessible larger{-}scale 2D
             topological surfaces uncover multiple skyrmion{-}like structures
             (i.e., vortex, anti{-}vortex, and flux{-}closure) and transition
             between them by adopting/designing different types of mechanical
             loadings (such as out{-}of{-}plane indention and air blowing).
             Topological surfaces with various designable reversible polar
             topological structures can be tailored by complex straining flexible
             2D materials, which provides excellent opportunities for
             next{-}generation nanoelectronics and
             sensor{~}devices.</jats:p>},
}

@Article{Bodenschatz_MaterBaselSwitz_2024_v17_p286,
    author =   {Cameron J. Bodenschatz and Wissam A. Saidi and Jamesa L. Stokes and
             Rebekah I. Webster and Gustavo Costa},
    title =    {{Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7
             Environmental Barrier Coatings Using a Deep Neural Network Potential
             and Comparison to Experiment}},
    journal =  {Mater. (Basel Switz.)},
    year =     2024,
    volume =   17,
    number =   2,
    pages =    286,
    doi =      {10.3390/ma17020286},
    abstract = {Environmental barrier coatings (EBCs) are an enabling technology for
             silicon carbide (SiC)-based ceramic matrix composites (CMCs) in
             extreme environments such as gas turbine engines. However, the
             development of new coating systems is hindered by the large design
             space and difficulty in predicting the properties for these materials.
             Density Functional Theory (DFT) has successfully been used to model
             and predict some thermodynamic and thermo-mechanical properties of
             high-temperature ceramics for EBCs, although these calculations are
             challenging due to their high computational costs. In this work, we
             use machine learning to train a deep neural network potential (DNP)
             for Y2Si2O7, which is then applied to calculate the thermodynamic and
             thermo-mechanical properties at near-DFT accuracy much faster and
             using less computational resources than DFT. We use this DNP to
             predict the phonon-based thermodynamic properties of Y2Si2O7 with good
             agreement to DFT and experiments. We also utilize the DNP to calculate
             the anisotropic, lattice direction-dependent coefficients of thermal
             expansion (CTEs) for Y2Si2O7. Molecular dynamics trajectories using
             the DNP correctly demonstrate the accurate prediction of the
             anisotropy of the CTE in good agreement with the diffraction
             experiments. In the future, this DNP could be applied to accelerate
             additional property calculations for Y2Si2O7 compared to DFT or
             experiments.},
}

@Article{You_MaterTodayPhys_2024_v40_p101282,
    author =   {Jiang You and Cheng Wang and Qi Wang and Min Zha and Hai-Long Jia and
             Jian Wang and Hui-Yuan Wang},
    title =    {{Accumulative coarse-graining of simple structural descriptors enables
             accurately predicting the dynamics of metallic liquids}},
    journal =  {Mater. Today Phys.},
    year =     2024,
    volume =   40,
    pages =    101282,
    doi =      {10.1016/j.mtphys.2023.101282},
}

@Article{Kamaeva_JMolLiq_2024_v393_p123659,
    author =   {L.V. Kamaeva and E.N. Tsiok and N.M. Chtchelkatchev},
    title =    {{Deep machine learning, molecular dynamics and experimental studies of
             liquid Al-Cu-Co alloys}},
    journal =  {J. Mol. Liq.},
    year =     2024,
    volume =   393,
    pages =    123659,
    doi =      {10.1016/j.molliq.2023.123659},
}

@Article{Jiang_JNuclMater_2024_v588_p154824,
    author =   {Peng Jiang and Rongyang Qiu and Jinli Cao and Xichuan Liao and
             Yangchun Chen and Zhixiao Liu and Xinfu He and Wen Yang and Huiqiu
             Deng},
    title =    {{Development of U-Zr-Xe ternary interatomic potentials appropriate for
             simulation of defect and Xe behaviors in U-Zr system}},
    journal =  {J. Nucl. Mater.},
    year =     2024,
    volume =   588,
    pages =    154824,
    doi =      {10.1016/j.jnucmat.2023.154824},
}

@Article{Li_IntJHeatMassTransf_2024_v225_p125404,
    author =   {Zhiqiang Li and Haoyu Dong and Jian Wang and Linhua Liu and Jia-Yue
             Yang},
    title =    {{Active learning molecular dynamics-assisted insights into ultralow
             thermal conductivity of two-dimensional covalent organic frameworks}},
    journal =  {Int. J. Heat Mass Transf.},
    year =     2024,
    volume =   225,
    pages =    125404,
    doi =      {10.1016/j.ijheatmasstransfer.2024.125404},
}

@Article{Benayad_ProcNatlAcadSciUSA_2024_v121_pe2322040121,
    author =   {Zakarya Benayad and Rolf David and Guillaume Stirnemann},
    title =    {{Prebiotic chemical reactivity in solution with quantum accuracy and
             microsecond sampling using neural network potentials}},
    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
    year =     2024,
    volume =   121,
    number =   23,
    pages =    {e2322040121},
    doi =      {10.1073/pnas.2322040121},
    abstract = {While RNA appears as a good candidate for the first autocatalytic
             systems preceding the emergence of modern life, the synthesis of RNA
             oligonucleotides without enzymes remains challenging. Because the
             uncatalyzed reaction is extremely slow, experimental studies bring
             limited and indirect information on the reaction mechanism, the nature
             of which remains debated. Here, we develop neural network potentials
             (NNPs) to study the phosphoester bond formation in water. While NNPs
             are becoming routinely applied to nonreactive systems or simple
             reactions, we demonstrate how they can systematically be trained to
             explore the reaction phase space for complex reactions involving
             several proton transfers and exchanges of heavy atoms. We then
             propagate at moderate computational cost hundreds of nanoseconds of a
             variety of enhanced sampling simulations with quantum accuracy in
             explicit solvent conditions. The thermodynamically preferred reaction
             pathway is a concerted, dissociative mechanism, with the transient
             formation of a metaphosphate transition state and direct participation
             of water solvent molecules that facilitate the exchange of protons
             through the nonbridging phosphate oxygens. Associative-dissociative
             pathways, characterized by a much tighter pentacoordinated phosphate,
             are higher in free energy. Our simulations also suggest that
             diprotonated phosphate, whose reactivity is never directly assessed in
             the experiments, is significantly less reactive than the
             monoprotonated species, suggesting that it is probably never the
             reactive species in normal pH conditions. These observations
             rationalize unexplained experimental results and the temperature
             dependence of the reaction rate, and they pave the way for the design
             of more efficient abiotic catalysts and activating groups.},
}

@Article{Xing_ChemEngJ_2024_v489_p151492,
    author =   {Zhihao Xing and Xi Jiang},
    title =    {{Neural network potential-based molecular investigation of pollutant
             formation of ammonia and ammonia-hydrogen combustion}},
    journal =  {Chem. Eng. J.},
    year =     2024,
    volume =   489,
    pages =    151492,
    doi =      {10.1016/j.cej.2024.151492},
}

@Article{Zhou_CemConcrRes_2024_v180_p107501,
    author =   {Ao Zhou and Juntao Kang and Renyuan Qin and Huali Hao and Tiejun Liu
             and Zechuan Yu},
    title =    {{Weaving the next-level structure of calcium silicate hydrate at the
             submicron scale via a remapping algorithm from coarse-grained to all-
             atom model}},
    journal =  {Cem. Concr. Res.},
    year =     2024,
    volume =   180,
    pages =    107501,
    doi =      {10.1016/j.cemconres.2024.107501},
}

@Article{Pelaez_JChemTheoryComput_2024_v20_p4076,
    author =   {Raul P. Pelaez and Guillem Simeon and Raimondas Galvelis and Antonio
             Mirarchi and Peter Eastman and Stefan Doerr and Philipp Th{\"o}lke and
             Thomas E. Markland and Gianni {\{}De Fabritiis{\}}},
    title =    {{TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular
             Simulations}},
    journal =  {J. Chem. Theory Comput.},
    year =     2024,
    volume =   20,
    number =   10,
    pages =    {4076--4087},
    doi =      {10.1021/acs.jctc.4c00253},
    abstract = {Achieving a balance between computational speed, prediction accuracy,
             and universal applicability in molecular simulations has been a
             persistent challenge. This paper presents substantial advancements in
             TorchMD-Net software, a pivotal step forward in the shift from
             conventional force fields to neural network-based potentials. The
             evolution of TorchMD-Net into a more comprehensive and versatile
             framework is highlighted, incorporating cutting-edge architectures
             such as TensorNet. This transformation is achieved through a modular
             design approach, encouraging customized applications within the
             scientific community. The most notable enhancement is a significant
             improvement in computational efficiency, achieving a very remarkable
             acceleration in the computation of energy and forces for TensorNet
             models, with performance gains ranging from 2{\texttimes} to
             10{\texttimes} over previous, nonoptimized, iterations. Other
             enhancements include highly optimized neighbor search algorithms that
             support periodic boundary conditions and smooth integration with
             existing molecular dynamics frameworks. Additionally, the updated
             version introduces the capability to integrate physical priors,
             further enriching its application spectrum and utility in research.
             The software is available at https://github.com/torchmd/torchmd-net.},
}

@Article{Villot_JChemPhys_2024_v160_p184103,
    author =   {Corentin Villot and Ka Un Lao},
    title =    {{Ab initio dispersion potentials based on physics-based functional
             forms with machine learning}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   18,
    pages =    184103,
    doi =      {10.1063/5.0204064},
    abstract = {In this study, we introduce SAPT10K, a comprehensive dataset
             comprising 9982 noncovalent interaction energies and their binding
             energy components (electrostatics, exchange, induction, and
             dispersion) for diverse intermolecular complexes of 944 unique dimers.
             These complexes cover significant portions of the intermolecular
             potential energy surface and were computed using higher-order
             symmetry-adapted perturbation theory, SAPT2+(3)(CCD), with a large
             aug-cc-pVTZ basis set. The dispersion energy values in SAPT10K serve
             as crucial inputs for refining the ab{~}initio dispersion potentials
             based on Grimme's D3 and many-body dispersion (MBD) models.
             Additionally, {\ensuremath{\Delta}} machine learning (ML) models based
             on newly developed intermolecular features, which are derived from
             intermolecular histograms of distances for element/substructure pairs
             to simultaneously account for local environments as well as long-range
             correlations, are also developed to address deficiencies of the D3/MBD
             models, including the inflexibility of their functional forms, the
             absence of MBD contributions in D3, and the standard Hirshfeld
             partitioning scheme used in MBD. The developed dispersion models can
             be applied to complexes involving a wide range of elements and charged
             monomers, surpassing other popular ML models, which are limited to
             systems with only neutral monomers and specific elements. The
             efficient D3-ML model, with Cartesian coordinates as the sole input,
             demonstrates promising results on a testing set comprising 6714
             dimers, outperforming another popular ML model, component-based
             machine-learned intermolecular force field (CLIFF), by 1.5 times.
             These refined D3/MBD-ML models have the capability to replace the
             time-consuming dispersion components in symmetry-adapted perturbation
             theory-based calculations and can promptly illustrate the dispersion
             contribution in noncovalent complexes for supramolecular assembly and
             chemical reactions.},
}

@Article{Zhong_JChemPhys_2024_v160_p124303,
    author =   {Shenghui Zhong and Zheyu Shi and Bin Zhang and Zhengcheng Wen and
             Longfei Chen},
    title =    {{Homogeneous water vapor condensation with a deep neural network
             potential model}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   12,
    pages =    124303,
    doi =      {10.1063/5.0189448},
    abstract = {Molecular-level nucleation has not been clearly understood due to the
             complexity of multi-body potentials and the stochastic, rare nature of
             the process. This work utilizes molecular dynamics (MD) simulations,
             incorporating a first-principles-based deep neural network (DNN)
             potential model, to investigate homogeneous water vapor condensation.
             The nucleation rates and critical nucleus sizes predicted by the DNN
             model are compared against commonly used semi-empirical models, namely
             extended simple point charge (SPC/E), TIP4P, and OPC, in addition to
             classical nucleation theory (CNT). The nucleation rates from the DNN
             model are comparable with those from the OPC model yet surpass the
             rates from the SPC/E and TIP4P models, a discrepancy that could mainly
             arise from the overestimated bulk free energy by SPC/E and TIP4P. The
             surface free energy predicted by CNT is lower than that in MD
             simulations, while its bulk free energy is higher than that in MD
             simulations, irrespective of the potential model used. Further
             analysis of cluster properties with the DNN model unveils pronounced
             variations of O-H bond length and H-O-H bond angle, along with
             averaged bond lengths and angles that are enlarged during embryonic
             cluster formation. Properties such as cluster surface free energy and
             liquid-to-vapor density transition profiles exhibit significant
             deviations from CNT assumptions.},
}

@Article{Hodapp_ComputMaterSci_2024_v233_p112715,
    author =   {M. Hodapp},
    title =    {{Machine learning is funny but physics makes the money: How machine-
             learning potentials can advance computer-aided materials design in
             metallurgy}},
    journal =  {Comput. Mater. Sci.},
    year =     2024,
    volume =   233,
    pages =    112715,
    doi =      {10.1016/j.commatsci.2023.112715},
}

@Article{Ding_JPhysChemLett_2024_v15_p616,
    author =   {Ye Ding and Jing Huang},
    title =    {{DP/MM: A Hybrid Model for Zinc-Protein Interactions in Molecular
             Dynamics}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2024,
    volume =   15,
    number =   2,
    pages =    {616--627},
    doi =      {10.1021/acs.jpclett.3c03158},
    abstract = {Zinc-containing proteins are vital for many biological processes, yet
             accurately modeling them using classical force fields is hindered by
             complicated polarization and charge transfer effects. This study
             introduces DP/MM, a hybrid force field scheme that utilizes a deep
             potential model to correct the atomic forces of zinc ions and their
             coordinated atoms, elevating them from MM to QM levels of accuracy.
             Trained on the difference between MM and QM atomic forces across
             diverse zinc coordination groups, the DP/MM model faithfully
             reproduces structural characteristics of zinc coordination during
             simulations, such as the tetrahedral coordination of Cys4 and Cys3His1
             groups. Furthermore, DP/MM allows water exchange in the zinc
             coordination environment. With its unique blend of accuracy,
             efficiency, flexibility, and transferability, DP/MM serves as a
             valuable tool for studying structures and dynamics of zinc-containing
             proteins and also represents a pioneering approach in the evolving
             landscape of machine learning potentials for molecular modeling.},
}

@Article{Liao_BiophysJ_2024_pS0006-34952400163-2,
    author =   {Jun Liao and Mincong Wu and Junyong Gao and Changjun Chen},
    title =    {{Calculation of solvation force in molecular dynamics simulation by
             deep-learning method}},
    journal =  {Biophys. J.},
    year =     2024,
    pages =    {S0006-3495(24)00163-2},
    doi =      {10.1016/j.bpj.2024.02.029},
    abstract = {Electrostatic calculations are generally used in studying the
             thermodynamics and kinetics of biomolecules in solvent. Generally,
             this is performed by solving the Poisson-Boltzmann equation on a large
             grid system, a process known to be time consuming. In this study, we
             developed a deep neural network to predict the decomposed solvation
             free energies and forces of all atoms in a molecule. To train the
             network, the internal coordinates of the molecule were used as the
             input data, and the solvation free energies along with transformed
             atomic forces from the Poisson-Boltzmann equation were used as labels.
             Both the training and prediction tasks were accelerated on GPU. Formal
             tests demonstrated that our method can provide reasonable predictions
             for small molecules when the network is well-trained with its
             simulation data. This method is suitable for processing lots of
             snapshots of molecules in a long trajectory. Moreover, we applied this
             method in the molecular dynamics simulation with enhanced sampling.
             The calculated free energy landscape closely resembled that obtained
             from explicit solvent simulations.},
}

@Article{Chen_MatterRadiatExtrem_2024_v9,
    author =   {Tao Chen and Qianrui Liu and Yu Liu and Liang Sun and Mohan Chen},
    title =    {{Combining stochastic density functional theory with deep potential
             molecular dynamics to study warm dense matter}},
    journal =  {Matter Radiat. Extrem.},
    year =     2024,
    volume =   9,
    number =   1,
    doi =      {10.1063/5.0163303},
    abstract = {{\ensuremath{<}}jats:p{\ensuremath{>}}In traditional finite-
             temperature Kohn{\textendash}Sham density functional theory (KSDFT),
             the partial occupation of a large number of high-energy KS eigenstates
             restricts the use of first-principles molecular dynamics methods at
             extremely high temperatures. However, stochastic density functional
             theory (SDFT) can overcome this limitation. Recently, SDFT and the
             related mixed stochastic{\textendash}deterministic density functional
             theory, based on a plane-wave basis set, have been implemented in the
             first-principles electronic structure software ABACUS [Q. Liu and M.
             Chen, Phys. Rev. B 106, 125132 (2022)]. In this study, we combine SDFT
             with the Born{\textendash}Oppenheimer molecular dynamics method to
             investigate systems with temperatures ranging from a few tens of eV to
             1000{~}eV. Importantly, we train machine-learning-based interatomic
             models using the SDFT data and employ these deep potential models to
             simulate large-scale systems with long trajectories. Subsequently, we
             compute and analyze the structural properties, dynamic properties, and
             transport coefficients of warm dense
             matter.{\ensuremath{<}}/jats:p{\ensuremath{>}}},
}
@Article{Yao_JacsAu_2024_v4_p992,
    author =   {Lin Yao and Wentao Guo and Zhen Wang and Shang Xiang and Wentan Liu
             and Guolin Ke},
    title =    {{Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning
             Approaches in Single-Step Retrosynthesis}},
    journal =  {Jacs Au},
    year =     2024,
    volume =   4,
    number =   3,
    pages =    {992--1003},
    doi =      {10.1021/jacsau.3c00737},
    abstract = {Single-step retrosynthesis in organic chemistry increasingly benefits
             from deep learning (DL) techniques in computer-aided synthesis design.
             While template-free DL models are flexible and promising for
             retrosynthesis prediction, they often ignore vital 2D molecular
             information and struggle with atom alignment for node generation,
             resulting in lower performance compared to the template-based and
             semi-template-based methods. To address these issues, we introduce
             node-aligned graph-to-graph (NAG2G), a transformer-based template-free
             DL model. NAG2G combines 2D molecular graphs and 3D conformations to
             retain comprehensive molecular details and incorporates product-
             reactant atom mapping through node alignment, which determines the
             order of the node-by-node graph outputs process in an autoregressive
             manner. Through rigorous benchmarking and detailed case studies, we
             have demonstrated that NAG2G stands out with its remarkable predictive
             accuracy on the expansive data sets of USPTO-50k and USPTO-FULL.
             Moreover, the model's practical utility is underscored by its
             successful prediction of synthesis pathways for multiple drug
             candidate molecules. This proves not only NAG2G's robustness but also
             its potential to revolutionize the prediction of complex chemical
             synthesis processes for future synthetic route design tasks.},
}
@Article{Luo_arXiv_2024_p2406.09841,
    author =   {Yizhen Luo and Kai Yang and Massimo Hong and Xing Yi Liu and Zikun Nie
             and Hao Zhou and Zaiqing Nie},
    title =    {{Learning Multi-view Molecular Representations with Structured and
             Unstructured Knowledge}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2406.09841},
    doi =      {10.48550/arXiv.2406.09841},
    abstract = {Capturing molecular knowledge with representation learning approaches
             holds significant potential in vast scientific fields such as
             chemistry and life science. An effective and generalizable molecular
             representation is expected to capture the consensus and complementary
             molecular expertise from diverse views and perspectives. However,
             existing works fall short in learning multi-view molecular
             representations, due to challenges in explicitly incorporating view
             information and handling molecular knowledge from heterogeneous
             sources. To address these issues, we present MV-Mol, a molecular
             representation learning model that harvests multi-view molecular
             expertise from chemical structures, unstructured knowledge from
             biomedical texts, and structured knowledge from knowledge graphs. We
             utilize text prompts to model view information and design a fusion
             architecture to extract view-based molecular representations. We
             develop a two-stage pre-training procedure, exploiting heterogeneous
             data of varying quality and quantity. Through extensive experiments,
             we show that MV-Mol provides improved representations that
             substantially benefit molecular property prediction. Additionally, MV-
             Mol exhibits state-of-the-art performance in multi-modal comprehension
             of molecular structures and texts. Code and data are available at
             https://github.com/PharMolix/OpenBioMed.},
}
@Article{Yang_Biorxiv_2024,
    author =   {Junwei Yang and Kangjie Zheng and Siyu Long and Zaiqing Nie and Ming
             Zhang and Xinyu Dai and Wei-Ying Ma and Hao Zhou},
    title =    {{Mol-AE: Auto-Encoder Based Molecular Representation Learning With 3D
             Cloze Test Objective}},
    journal =  {Biorxiv},
    year =     2024,
    doi =      {10.1101/2024.04.13.589331},
    abstract = {Abstract3D molecular representation learning has gained tremendous
             interest and achieved promising performance in various downstream
             tasks. A series of recent approaches follow a prevalent framework: an
             encoder-only model coupled with a coordinate denoising objective.
             However, through a series of analytical experiments, we prove that the
             encoderonly model with coordinate denoising objective exhibits
             inconsistency between pre-training and downstream objectives, as well
             as issues with disrupted atomic identifiers. To address these two
             issues, we propose Mol-AE for molecular representation learning, an
             auto-encoder model using positional encoding as atomic identifiers. We
             also propose a new training objective named 3D Cloze Test to make the
             model learn better atom spatial relationships from real molecular
             substructures. Empirical results demonstrate that Mol-AE achieves a
             large margin performance gain compared to the current state-of-the-art
             3D molecular modeling approach. The source codes of Mol-AE are
             publicly available athttps://github.com/yjwtheonly/MolAE.},
}
@Article{Feng_arXiv_2024_p2406.17797,
    author =   {Shikun Feng and Jiaxin Zheng and Yinjun Jia and Yanwen Huang and
             Fengfeng Zhou and Wei-Ying Ma and Yanyan Lan},
    title =    {{MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-
             Target Binding Analysis}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2406.17797},
    doi =      {10.48550/arXiv.2406.17797},
    abstract = {Molecular representation learning is pivotal for various molecular
             property prediction tasks related to drug discovery. Robust and
             accurate benchmarks are essential for refining and validating current
             methods. Existing molecular property benchmarks derived from wet
             experiments, however, face limitations such as data volume
             constraints, unbalanced label distribution, and noisy labels. To
             address these issues, we construct a large-scale and precise molecular
             representation dataset of approximately 140,000 small molecules,
             meticulously designed to capture an extensive array of chemical,
             physical, and biological properties, derived through a robust
             computational ligand-target binding analysis pipeline. We conduct
             extensive experiments on various deep learning models, demonstrating
             that our dataset offers significant physicochemical interpretability
             to guide model development and design. Notably, the dataset's
             properties are linked to binding affinity metrics, providing
             additional insights into model performance in drug-target interaction
             tasks. We believe this dataset will serve as a more accurate and
             reliable benchmark for molecular representation learning, thereby
             expediting progress in the field of artificial intelligence-driven
             drug discovery.},
}
@Article{Li_arXiv_2024_p2401.13923,
    author =   {Sihang Li and Zhiyuan Liu and Yanchen Luo and Xiang Wang and Xiangnan
             He and Kenji Kawaguchi and Tat-Seng Chua and Qi Tian},
    title =    {{Towards 3D Molecule-Text Interpretation in Language Models}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2401.13923},
    doi =      {10.48550/arXiv.2401.13923},
    abstract = {Language Models (LMs) have greatly influenced diverse domains.
             However, their inherent limitation in comprehending 3D molecular
             structures has considerably constrained their potential in the
             biomolecular domain. To bridge this gap, we focus on 3D molecule-text
             interpretation, and propose 3D-MoLM: 3D-Molecular Language Modeling.
             Specifically, 3D-MoLM enables an LM to interpret and analyze 3D
             molecules by equipping the LM with a 3D molecular encoder. This
             integration is achieved by a 3D molecule-text projector, bridging the
             3D molecular encoder's representation space and the LM's input space.
             Moreover, to enhance 3D-MoLM's ability of cross-modal molecular
             understanding and instruction following, we meticulously curated a 3D
             molecule-centric instruction tuning dataset -- 3D-MoIT. Through 3D
             molecule-text alignment and 3D molecule-centric instruction tuning,
             3D-MoLM establishes an integration of 3D molecular encoder and LM. It
             significantly surpasses existing baselines on downstream tasks,
             including molecule-text retrieval, molecule captioning, and more
             challenging open-text molecular QA tasks, especially focusing on
             3D-dependent properties. We release our codes and datasets at
             https://github.com/lsh0520/3D-MoLM.},
}
@Article{Pei_arXiv_2024_p2406.05797,
    author =   {Qizhi Pei and Lijun Wu and Kaiyuan Gao and Jinhua Zhu and Rui Yan},
    title =    {{3D-MolT5: Towards Unified 3D Molecule-Text Modeling with 3D Molecular
             Tokenization}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2406.05797},
    doi =      {10.48550/arXiv.2406.05797},
    abstract = {The integration of molecule and language has garnered increasing
             attention in molecular science. Recent advancements in Language Models
             (LMs) have demonstrated potential for the comprehensive modeling of
             molecule and language. However, existing works exhibit notable
             limitations. Most existing works overlook the modeling of 3D
             information, which is crucial for understanding molecular structures
             and also functions. While some attempts have been made to leverage
             external structure encoding modules to inject the 3D molecular
             information into LMs, there exist obvious difficulties that hinder the
             integration of molecular structure and language text, such as modality
             alignment and separate tuning. To bridge this gap, we propose
             3D-MolT5, a unified framework designed to model both 1D molecular
             sequence and 3D molecular structure. The key innovation lies in our
             methodology for mapping fine-grained 3D substructure representations
             (based on 3D molecular fingerprints) to a specialized 3D token
             vocabulary for 3D-MolT5. This 3D structure token vocabulary enables
             the seamless combination of 1D sequence and 3D structure
             representations in a tokenized format, allowing 3D-MolT5 to encode
             molecular sequence (SELFIES), molecular structure, and text sequences
             within a unified architecture. Alongside, we further introduce 1D and
             3D joint pre-training to enhance the model's comprehension of these
             diverse modalities in a joint representation space and better
             generalize to various tasks for our foundation model. Through
             instruction tuning on multiple downstream datasets, our proposed
             3D-MolT5 shows superior performance than existing methods in molecular
             property prediction, molecule captioning, and text-based molecule
             generation tasks. Our code will be available on GitHub soon.},
}
@Article{Gao_arXiv_2024_p2406.08980,
    author =   {Bowen Gao and Haichuan Tan and Yanwen Huang and Minsi Ren and Xiao
             Huang and Wei-Ying Ma and Ya-Qin Zhang and Yanyan Lan},
    title =    {{From Theory to Therapy: Reframing SBDD Model Evaluation via Practical
             Metrics}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2406.08980},
    doi =      {10.48550/arXiv.2406.08980},
    abstract = {Recent advancements in structure-based drug design (SBDD) have
             significantly enhanced the efficiency and precision of drug discovery
             by generating molecules tailored to bind specific protein pockets.
             Despite these technological strides, their practical application in
             real-world drug development remains challenging due to the
             complexities of synthesizing and testing these molecules. The
             reliability of the Vina docking score, the current standard for
             assessing binding abilities, is increasingly questioned due to its
             susceptibility to overfitting. To address these limitations, we
             propose a comprehensive evaluation framework that includes assessing
             the similarity of generated molecules to known active compounds,
             introducing a virtual screening-based metric for practical deployment
             capabilities, and re-evaluating binding affinity more rigorously. Our
             experiments reveal that while current SBDD models achieve high Vina
             scores, they fall short in practical usability metrics, highlighting a
             significant gap between theoretical predictions and real-world
             applicability. Our proposed metrics and dataset aim to bridge this
             gap, enhancing the practical applicability of future SBDD models and
             aligning them more closely with the needs of pharmaceutical research
             and development.},
}
@Article{Xiao_arXiv_2024_p2403.08167,
    author =   {Teng Xiao and Chao Cui and Huaisheng Zhu and Vasant G. Honavar},
    title =    {{MolBind: Multimodal Alignment of Language, Molecules, and Proteins}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2403.08167},
    doi =      {10.48550/arXiv.2403.08167},
    abstract = {Recent advancements in biology and chemistry have leveraged multi-
             modal learning, integrating molecules and their natural language
             descriptions to enhance drug discovery. However, current pre-training
             frameworks are limited to two modalities, and designing a unified
             network to process different modalities (e.g., natural language, 2D
             molecular graphs, 3D molecular conformations, and 3D proteins) remains
             challenging due to inherent gaps among them. In this work, we propose
             MolBind, a framework that trains encoders for multiple modalities
             through contrastive learning, mapping all modalities to a shared
             feature space for multi-modal semantic alignment. To facilitate
             effective pre-training of MolBind on multiple modalities, we also
             build and collect a high-quality dataset with four modalities,
             MolBind-M4, including graph-language, conformation-language, graph-
             conformation, and conformation-protein paired data. MolBind shows
             superior zero-shot learning performance across a wide range of tasks,
             demonstrating its strong capability of capturing the underlying
             semantics of multiple modalities.},
}
@Article{Zhang_IntJComputIntellSyst_2024_v17_p165,
    author =   {Yi-Lun Zhang and Wen-Tao Wang and Jia-Hui Guan and Deepak Kumar Jain
             and Tian-Yang Wang and Swalpa Kumar Roy},
    title =    {{MocFormer: A Two-Stage Pre-training-Driven Transformer for
             Drug{\textendash}Target Interactions Prediction}},
    journal =  {Int J Comput. Intell Syst},
    year =     2024,
    volume =   17,
    number =   1,
    pages =    165,
    doi =      {10.1007/s44196-024-00561-1},
    abstract = {AbstractDrug{\textendash}target interactions is essential for
             advancing pharmaceuticals. Traditional drug{\textendash}target
             interaction studies rely on labor-intensive laboratory techniques.
             Still, recent advancements in computing power have elevated the
             importance of deep learning methods, offering faster, more precise,
             and cost-effective screening and prediction. Nonetheless, general deep
             learning methods often yield low-confidence results due to the complex
             nature of drugs and proteins, bias, limited labeled data, and feature
             extraction challenges. To address these challenges, a novel two-stage
             pre-trained framework is proposed for drug{\textendash}target
             interactions prediction. In the first stage, pre-trained molecule and
             protein models develop a comprehensive feature representation,
             enhancing the framework{\textquoteright}s ability to handle drug and
             protein diversity. This also reduces bias, improving prediction
             accuracy. In the second stage, a transformer with bilinear pooling and
             a fully connected layer enables predictions based on feature vectors.
             Comprehensive experiments were conducted using public datasets from
             DrugBank and Epigenetic-regulators datasets to evaluate the
             framework{\textquoteright}s effectiveness. The results demonstrate
             that the proposed framework outperforms the state-of-the-art methods
             regarding accuracy, area under the receiver operating characteristic
             curve, recall, and area under the precision-recall curve. The code is
             available at: https://github.com/DHCGroup/MocFormer.},
}
@Article{Feng_arXiv_2023_p2311.16160,
    author =   {Shikun Feng and Minghao Li and Yinjun Jia and Weiying Ma and Yanyan
             Lan},
    title =    {{Protein-ligand binding representation learning from fine-grained
             interactions}},
    journal =  {arXiv},
    year =     2023,
    pages =    {2311.16160},
    doi =      {10.48550/arXiv.2311.16160},
    abstract = {The binding between proteins and ligands plays a crucial role in the
             realm of drug discovery. Previous deep learning approaches have shown
             promising results over traditional computationally intensive methods,
             but resulting in poor generalization due to limited supervised data.
             In this paper, we propose to learn protein-ligand binding
             representation in a self-supervised learning manner. Different from
             existing pre-training approaches which treat proteins and ligands
             individually, we emphasize to discern the intricate binding patterns
             from fine-grained interactions. Specifically, this self-supervised
             learning problem is formulated as a prediction of the conclusive
             binding complex structure given a pocket and ligand with a Transformer
             based interaction module, which naturally emulates the binding
             process. To ensure the representation of rich binding information, we
             introduce two pre-training tasks, i.e.{\textasciitilde}atomic pairwise
             distance map prediction and mask ligand reconstruction, which
             comprehensively model the fine-grained interactions from both
             structure and feature space. Extensive experiments have demonstrated
             the superiority of our method across various binding tasks, including
             protein-ligand affinity prediction, virtual screening and protein-
             ligand docking.},
}
@Article{Gao_arXiv_2024_p2310.07229,
    author =   {Bowen Gao and Yinjun Jia and Yuanle Mo and Yuyan Ni and Weiying Ma and
             Zhiming Ma and Yanyan Lan},
    title =    {{ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-
             Surroundings Alignment}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2310.07229},
    doi =      {10.48550/arXiv.2310.07229},
    abstract = {Pocket representations play a vital role in various biomedical
             applications, such as druggability estimation, ligand affinity
             prediction, and de novo drug design. While existing geometric features
             and pretrained representations have demonstrated promising results,
             they usually treat pockets independent of ligands, neglecting the
             fundamental interactions between them. However, the limited pocket-
             ligand complex structures available in the PDB database (less than 100
             thousand non-redundant pairs) hampers large-scale pretraining
             endeavors for interaction modeling. To address this constraint, we
             propose a novel pocket pretraining approach that leverages knowledge
             from high-resolution atomic protein structures, assisted by highly
             effective pretrained small molecule representations. By segmenting
             protein structures into drug-like fragments and their corresponding
             pockets, we obtain a reasonable simulation of ligand-receptor
             interactions, resulting in the generation of over 5 million complexes.
             Subsequently, the pocket encoder is trained in a contrastive manner to
             align with the representation of pseudo-ligand furnished by some
             pretrained small molecule encoders. Our method, named ProFSA, achieves
             state-of-the-art performance across various tasks, including pocket
             druggability prediction, pocket matching, and ligand binding affinity
             prediction. Notably, ProFSA surpasses other pretraining methods by a
             substantial margin. Moreover, our work opens up a new avenue for
             mitigating the scarcity of protein-ligand complex data through the
             utilization of high-quality and diverse protein structure databases.},
}
@Article{Zhang_GreenChem_2024_v26_p4181,
    author =   {Jun Zhang and Qin Wang and Yang Lei and Weifeng Shen},
    title =    {{An interpretable 3D multi-hierarchical representation-based deep
             neural network for environmental, health and safety properties
             prediction of organic solvents}},
    journal =  {Green Chem.},
    year =     2024,
    volume =   26,
    number =   7,
    pages =    {4181--4191},
    doi =      {10.1039/D3GC04801B},
    abstract = {A 3D multi-hierarchical representation-based deep neural network
             (3D-MrDNN) architecture for prediction of the environmental, health
             and safety properties of organic solvents.},
}
@Article{Comajuncosa-Creus_JCheminformatics_2024_v16_p70,
    author =   {Arnau Comajuncosa-Creus and Aksel Lenes and Miguel S{\'a}nchez-
             Palomino and Dylan Dalton and Patrick Aloy},
    title =    {{Stereochemically-aware bioactivity descriptors for uncharacterized
             chemical compounds}},
    journal =  {J. Cheminformatics},
    year =     2024,
    volume =   16,
    number =   1,
    pages =    70,
    doi =      {10.1186/s13321-024-00867-4},
    abstract = {Stereochemistry plays a fundamental role in pharmacology. Here, we
             systematically investigate the relationship between stereoisomerism
             and bioactivity on over 1{~}M compounds, finding that a very
             significant fraction ({\textasciitilde}{\,}40{\%}) of spatial isomer
             pairs show, to some extent, distinct bioactivities. We then use the 3D
             representation of these molecules to train a collection of deep neural
             networks (Signaturizers3D) to generate bioactivity descriptors
             associated to small molecules, that capture their effects at
             increasing levels of biological complexity (i.e. from protein targets
             to clinical outcomes). Further, we assess the ability of the
             descriptors to distinguish between stereoisomers and to recapitulate
             their different target binding profiles. Overall, we show how these
             new stereochemically-aware descriptors provide an even more faithful
             description of complex small molecule bioactivity properties,
             capturing key differences in the activity of stereoisomers.Scientific
             contributionWe systematically assess the relationship between
             stereoisomerism and bioactivity on a large scale, focusing on
             compound-target binding events, and use our findings to train novel
             deep learning models to generate stereochemically-aware bioactivity
             signatures for any compound of interest.},
}
@Article{Chang_JChemInfModel_2024_v64_p3149,
    author =   {Jiamin Chang and Xiaoyu Fan and Boxue Tian},
    title =    {{DeepP450: Predicting Human P450 Activities of Small Molecules by
             Integrating Pretrained Protein Language Model and Molecular
             Representation}},
    journal =  {J. Chem. Inf. Model.},
    year =     2024,
    volume =   64,
    number =   8,
    pages =    {3149--3160},
    doi =      {10.1021/acs.jcim.4c00115},
    abstract = {Cytochrome P450 enzymes (CYPs) play a crucial role in Phase I drug
             metabolism in the human body, and CYP activity toward compounds can
             significantly affect druggability, making early prediction of CYP
             activity and substrate identification essential for therapeutic
             development. Here, we established a deep learning model for assessing
             potential CYP substrates, DeepP450, by fine-tuning protein and
             molecule pretrained models through feature integration with cross-
             attention and self-attention layers. This model exhibited high
             prediction accuracy (0.92) on the test set, with area under the
             receiver operating characteristic curve (AUROC) values ranging from
             0.89 to 0.98 in substrate/nonsubstrate predictions across the nine
             major human CYPs, surpassing current benchmarks for CYP activity
             prediction. Notably, DeepP450 uses only one model to predict
             substrates/nonsubstrates for any of the nine CYPs and exhibits certain
             generalizability on novel compounds and different categories of human
             CYPs, which could greatly facilitate early stage drug design by
             avoiding CYP-reactive compounds.},
}
@Article{Luo_JacsAu_2024_v4_p3451,
    author =   {Weiliang Luo and Gengmo Zhou and Zhengdan Zhu and Yannan Yuan and
             Guolin Ke and Zhewei Wei and Zhifeng Gao and Hang Zheng},
    title =    {{Bridging Machine Learning and Thermodynamics for Accurate pK a
             Prediction}},
    journal =  {Jacs Au},
    year =     2024,
    volume =   4,
    number =   9,
    pages =    {3451--3465},
    doi =      {10.1021/jacsau.4c00271},
    abstract = {Integrating scientific principles into machine learning models to
             enhance their predictive performance and generalizability is a central
             challenge in the development of AI for Science. Herein, we introduce
             Uni-pK a, a novel framework that successfully incorporates
             thermodynamic principles into machine learning modeling, achieving
             high-precision predictions of acid dissociation constants (pK a), a
             crucial task in the rational design of drugs and catalysts, as well as
             a modeling challenge in computational physical chemistry for small
             organic molecules. Uni-pK a utilizes a comprehensive free energy model
             to represent molecular protonation equilibria accurately. It features
             a structure enumerator that reconstructs molecular configurations from
             pK a data, coupled with a neural network that functions as a free
             energy predictor, ensuring high-throughput, data-driven prediction
             while preserving thermodynamic consistency. Employing a pretraining-
             finetuning strategy with both predicted and experimental pK a data,
             Uni-pK a not only achieves state-of-the-art accuracy in
             chemoinformatics but also shows comparable precision to quantum
             mechanics-based methods.},
}
@Article{Wang_NatCommun_2024_v15_p1904,
    author =   {Jingqi Wang and Jiapeng Liu and Hongshuai Wang and Musen Zhou and
             Guolin Ke and Linfeng Zhang and Jianzhong Wu and Zhifeng Gao and
             Diannan Lu},
    title =    {{A comprehensive transformer-based approach for high-accuracy gas
             adsorption predictions in metal-organic frameworks}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    1904,
    doi =      {10.1038/s41467-024-46276-x},
    abstract = {Gas separation is crucial for industrial production and environmental
             protection, with metal-organic frameworks (MOFs) offering a promising
             solution due to their tunable structural properties and chemical
             compositions. Traditional simulation approaches, such as molecular
             dynamics, are complex and computationally demanding. Although feature
             engineering-based machine learning methods perform better, they are
             susceptible to overfitting because of limited labeled data.
             Furthermore, these methods are typically designed for single tasks,
             such as predicting gas adsorption capacity under specific conditions,
             which restricts the utilization of comprehensive datasets including
             all adsorption capacities. To address these challenges, we propose
             Uni-MOF, an innovative framework for large-scale, three-dimensional
             MOF representation learning, designed for multi-purpose gas
             prediction. Specifically, Uni-MOF serves as a versatile gas adsorption
             estimator for MOF materials, employing pure three-dimensional
             representations learned from over 631,000 collected MOF and COF
             structures. Our experimental results show that Uni-MOF can
             automatically extract structural representations and predict
             adsorption capacities under various operating conditions using a
             single model. For simulated data, Uni-MOF exhibits remarkably high
             predictive accuracy across all datasets. Additionally, the values
             predicted by Uni-MOF correspond with the outcomes of adsorption
             experiments. Furthermore, Uni-MOF demonstrates considerable potential
             for broad applicability in predicting a wide array of other
             properties.},
}
@Article{Xiao_arXiv_2024_p2411.10821,
    author =   {Teng Xiao and Chao Cui and Huaisheng Zhu and Vasant G. Honavar},
    title =    {{GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2411.10821},
    doi =      {10.48550/arXiv.2411.10821},
    abstract = {Pretraining molecular representations is crucial for drug and material
             discovery. Recent methods focus on learning representations from
             geometric structures, effectively capturing 3D position information.
             Yet, they overlook the rich information in biomedical texts, which
             detail molecules' properties and substructures. With this in mind, we
             set up a data collection effort for 200K pairs of ground-state
             geometric structures and biomedical texts, resulting in a PubChem3D
             dataset. Based on this dataset, we propose the GeomCLIP framework to
             enhance for multi-modal representation learning from molecular
             structures and biomedical text. During pre-training, we design two
             types of tasks, i.e., multimodal representation alignment and unimodal
             denoising pretraining, to align the 3D geometric encoder with textual
             information and, at the same time, preserve its original
             representation power. Experimental results show the effectiveness of
             GeomCLIP in various tasks such as molecular property prediction, zero-
             shot text-molecule retrieval, and 3D molecule captioning. Our code and
             collected dataset are available at
             {\textbackslash}url{\{}https://github.com/xiaocui3737/GeomCLIP{\}}},
}
@Article{Cheng_AdvOptMater_2023_v11,
    author =   {Zheng Cheng and Jiapeng Liu and Tong Jiang and Mohan Chen and Fuzhi
             Dai and Zhifeng Gao and Guolin Ke and Zifeng Zhao and Qi Ou},
    title =    {{Automatic Screen{-}out of Ir(III) Complex Emitters by Combined Machine
             Learning and Computational Analysis}},
    journal =  {Adv. Opt. Mater.},
    year =     2023,
    volume =   11,
    number =   18,
    doi =      {10.1002/adom.202301093},
    abstract = {AbstractThe organic light{-}emitting diode (OLED) has gained
             widespread commercial use, yet there is a continuous need to identify
             innovative emitters that offer higher efficiency and a broader color
             gamut. To effectively screen out promising OLED molecules that are yet
             to be synthesized, representation learning aided high throughput
             virtual screening (HTVS) over millions of Ir(III) complexes, which are
             prototypical types of phosphorescent OLED material constructed via a
             random combination of 278 reported ligands. This study successfully
             screens out a decent amount of promising candidates for both display
             and lighting purposes, which are worth further experimental
             investigation. The high efficiency and accuracy of this model are
             largely attributed to the pioneering attempt of using representation
             learning to organic luminescent molecules, which is initiated by a
             pre{-}training procedure with over 1.6 million 3D molecular structures
             and frontier orbital energies predicted via semi{-}empirical methods,
             followed by a fine{-}tuning scheme via the quantum mechanical computed
             properties over around 1500 candidates. Such workflow enables an
             effective model construction process that is otherwise hindered by the
             scarcity of labeled data and can be straightforwardly extended to the
             discovery of other novel materials.},
}
@Article{Yao_JacsAu_2024_v4_p992,
    author =   {Lin Yao and Wentao Guo and Zhen Wang and Shang Xiang and Wentan Liu
             and Guolin Ke},
    title =    {{Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning
             Approaches in Single-Step Retrosynthesis}},
    journal =  {Jacs Au},
    year =     2024,
    volume =   4,
    number =   3,
    pages =    {992--1003},
    doi =      {10.1021/jacsau.3c00737},
    abstract = {Single-step retrosynthesis in organic chemistry increasingly benefits
             from deep learning (DL) techniques in computer-aided synthesis design.
             While template-free DL models are flexible and promising for
             retrosynthesis prediction, they often ignore vital 2D molecular
             information and struggle with atom alignment for node generation,
             resulting in lower performance compared to the template-based and
             semi-template-based methods. To address these issues, we introduce
             node-aligned graph-to-graph (NAG2G), a transformer-based template-free
             DL model. NAG2G combines 2D molecular graphs and 3D conformations to
             retain comprehensive molecular details and incorporates product-
             reactant atom mapping through node alignment, which determines the
             order of the node-by-node graph outputs process in an autoregressive
             manner. Through rigorous benchmarking and detailed case studies, we
             have demonstrated that NAG2G stands out with its remarkable predictive
             accuracy on the expansive data sets of USPTO-50k and USPTO-FULL.
             Moreover, the model's practical utility is underscored by its
             successful prediction of synthesis pathways for multiple drug
             candidate molecules. This proves not only NAG2G's robustness but also
             its potential to revolutionize the prediction of complex chemical
             synthesis processes for future synthetic route design tasks.},
}
@Article{Lu_arXiv_2023_p2303.16982,
    author =   {Shuqi Lu and Zhifeng Gao and Di He and Linfeng Zhang and Guolin Ke},
    title =    {{Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+}},
    journal =  {arXiv},
    year =     2023,
    pages =    {2303.16982},
    doi =      {10.48550/arXiv.2303.16982},
    abstract = {Recent developments in deep learning have made remarkable progress in
             speeding up the prediction of quantum chemical (QC) properties by
             removing the need for expensive electronic structure calculations like
             density functional theory. However, previous methods learned from 1D
             SMILES sequences or 2D molecular graphs failed to achieve high
             accuracy as QC properties primarily depend on the 3D equilibrium
             conformations optimized by electronic structure methods, far different
             from the sequence-type and graph-type data. In this paper, we propose
             a novel approach called Uni-Mol+ to tackle this challenge. Uni-Mol+
             first generates a raw 3D molecule conformation from inexpensive
             methods such as RDKit. Then, the raw conformation is iteratively
             updated to its target DFT equilibrium conformation using neural
             networks, and the learned conformation will be used to predict the QC
             properties. To effectively learn this update process towards the
             equilibrium conformation, we introduce a two-track Transformer model
             backbone and train it with the QC property prediction task. We also
             design a novel approach to guide the model's training process. Our
             extensive benchmarking results demonstrate that the proposed Uni-Mol+
             significantly improves the accuracy of QC property prediction in
             various datasets. We have made the code and model publicly available
             at {\textbackslash}url{\{}https://github.com/dptech-corp/Uni-Mol{\}}.},
}
@Article{Gao_arXiv_2023_p2304.12239,
    author =   {Zhifeng Gao and Xiaohong Ji and Guojiang Zhao and Hongshuai Wang and
             Hang Zheng and Guolin Ke and Linfeng Zhang},
    title =    {{Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction}},
    journal =  {arXiv},
    year =     2023,
    pages =    {2304.12239},
    doi =      {10.48550/arXiv.2304.12239},
    abstract = {Recently deep learning based quantitative structure-activity
             relationship (QSAR) models has shown surpassing performance than
             traditional methods for property prediction tasks in drug discovery.
             However, most DL based QSAR models are restricted to limited labeled
             data to achieve better performance, and also are sensitive to model
             scale and hyper-parameters. In this paper, we propose Uni-QSAR, a
             powerful Auto-ML tool for molecule property prediction tasks. Uni-QSAR
             combines molecular representation learning (MRL) of 1D sequential
             tokens, 2D topology graphs, and 3D conformers with pretraining models
             to leverage rich representation from large-scale unlabeled data.
             Without any manual fine-tuning or model selection, Uni-QSAR
             outperforms SOTA in 21/22 tasks of the Therapeutic Data Commons (TDC)
             benchmark under designed parallel workflow, with an average
             performance improvement of 6.09{\textbackslash}{\%}. Furthermore, we
             demonstrate the practical usefulness of Uni-QSAR in drug discovery
             domains.},
}
@Article{Wang_arXiv_2024_p2406.04727,
    author =   {Fanmeng Wang and Wentao Guo and Minjie Cheng and Shen Yuan and
             Hongteng Xu and Zhifeng Gao},
    title =    {{MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer
             Property Prediction}},
    journal =  {arXiv},
    year =     2024,
    pages =    {2406.04727},
    doi =      {10.48550/arXiv.2406.04727},
    abstract = {Polymers are high-molecular-weight compounds constructed by the
             covalent bonding of numerous identical or similar monomers so that
             their 3D structures are complex yet exhibit unignorable regularity.
             Typically, the properties of a polymer, such as plasticity,
             conductivity, bio-compatibility, and so on, are highly correlated with
             its 3D structure. However, existing polymer property prediction
             methods heavily rely on the information learned from polymer SMILES
             sequences (P-SMILES strings) while ignoring crucial 3D structural
             information, resulting in sub-optimal performance. In this work, we
             propose MMPolymer, a novel multimodal multitask pretraining framework
             incorporating polymer 1D sequential and 3D structural information to
             encourage downstream polymer property prediction tasks. Besides,
             considering the scarcity of polymer 3D data, we further introduce the
             {''}Star Substitution{''} strategy to extract 3D structural
             information effectively. During pretraining, in addition to predicting
             masked tokens and recovering clear 3D coordinates, MMPolymer achieves
             the cross-modal alignment of latent representations. Then we further
             fine-tune the pretrained MMPolymer for downstream polymer property
             prediction tasks in the supervised learning paradigm. Experiments show
             that MMPolymer achieves state-of-the-art performance in downstream
             property prediction tasks. Moreover, given the pretrained MMPolymer,
             utilizing merely a single modality in the fine-tuning phase can also
             outperform existing methods, showcasing the exceptional capability of
             MMPolymer in polymer feature extraction and utilization.},
}
+@Article{Zeng_NatCommun_2020_v11_p5713,
  author =   {Jinzhe Zeng and Liqun Cao and Mingyuan Xu and Tong Zhu* and John Z. H. Zhang*},
  title =    {{Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation}},
  journal =  {Nature Communications},
  year =     2020,
  key =      {combustion},
  volume =   11,
  pages =    5713,
  annote =   {Combustion is a complex chemical system which involves thousands
    of chemical reactions and generates hundreds of molecular species and
    radicals during the process. In this work, a neural network-based molecular
    dynamics (MD) simulation is carried out to simulate the benchmark combustion
    of methane. During MD simulation, detailed reaction processes leading to the
    creation of specific molecular species including various intermediate
    radicals and the products are intimately revealed and characterized.
    Overall, a total of 798 different chemical reactions were recorded and some
    new chemical reaction pathways were discovered. We believe that the present
    work heralds the dawn of a new era in which neural network-based reactive MD
    simulation can be practically applied to simulating important complex
    reaction systems at ab initio level, which provides atomic-level
    understanding of chemical reaction processes as well as discovery of new
    reaction pathways at an unprecedented level of detail beyond what laboratory
    experiments could accomplish.},
  doi = {10.1038/s41467-020-19497-z},
  googlescholar = 11435320914094498497,
  researchgate = 345758546,
  pdf = {https://www.nature.com/articles/s41467-020-19497-z.pdf},
  github = {tongzhugroup/mddatasetbuilder},
  image = {https://media.springernature.com/w75h75/springer-static/image/art%3A10.1038%2Fs41467-020-19497-z/MediaObjects/41467_2020_19497_Fig1_HTML.png}
}

@Article{Zeng_PhysChemChemPhys_2020_v22_p683,
  author =   {Jinzhe Zeng and Liqun Cao and Chih-Hao Chin* and Haisheng Ren* and John Z. H. Zhang and Tong Zhu*},
  title =    {{ReacNetGenerator: an automatic reaction network
              generator for reactive molecular dynamics
              simulations}},
  journal =  {Physical Chemistry Chemical Physics},
  year =     2020,
  volume =   22,
  number =   2,
  pages =    {683--691},
  abstract =     {Reactive molecular dynamics (MD) simulation makes
              it possible to study the reaction mechanism of complex
              reaction systems at the atomic level. However, the
          analysis of MD trajectories which contain thousands of
          species and reaction pathways has become a major
          obstacle to the application of reactive MD simulation
          in large-scale systems. Here, we report the development
          and application of the Reaction Network Generator
          (ReacNetGenerator) method. It can automatically extract
          the reaction network from the reaction trajectory
          without any predefined reaction coordinates and
          elementary reaction steps. Molecular species can be
          automatically identified from the cartesian coordinates
          of atoms and the hidden Markov model is used to filter
          the trajectory noises which makes the analysis process
          easier and more accurate. The ReacNetGenerator has been
          successfully used to analyze the reactive MD
          trajectories of the combustion of methane and
          4-component surrogate fuel for rocket propellant 3
          (RP-3), and it has great advantages in terms of
          efficiency and accuracy compared to traditional manual
          analysis.},
  doi =      {10.1039/C9CP05091D},
  eprint =   {https://doi.org/10.1039/C9CP05091D},
  googlescholar = 11110115133859772670,
  github = {tongzhugroup/reacnetgenerator},
  researchgate=337541969,
  pdf = {https://scholarship.libraries.rutgers.edu/view/pdfCoverPage?instCode=01RUT_INST&filePid=13678734780004646&download=true}
}

@article{Zeng_EnergyFuels_2021_v35_p762,
  author = {Jinzhe Zeng and Linfeng Zhang* and Han Wang* and Tong Zhu*},
  title = {{Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator}},
  journal = {Energy & Fuels},
  volume = 35,
  number = 1,
  pages = {762--769},
  year = 2021,
  doi = {10.1021/acs.energyfuels.0c03211},
  annote = { Reactive molecular dynamics (MD) simulation is a powerful tool to study the reaction mechanism of
             complex chemical systems. Central to the method is the potential energy surface (PES) that can
             describe the breaking and formation of chemical bonds. The development of both accurate and
             efficient PES has attracted significant effort in the past 2 decades. A recently developed deep
             potential (DP) model has the promise to bring ab initio accuracy to large-scale reactive MD
             simulations. However, for complex chemical reaction processes like pyrolysis, it remains
             challenging to generate reliable DP models with an optimal training data set. In this work, a
             data set construction scheme for such a purpose was established. The employment of a concurrent
             learning algorithm allows us to maximize the exploration of the chemical space while minimizing
             the redundancy of the data set. This greatly reduces the cost of computational resources required
             for ab initio calculations. Based on this method, we constructed a data set for the pyrolysis of
             n-dodecane, which contains 35 496 structures. The reactive MD simulation with the DP model trained
             based on this data set revealed the pyrolysis mechanism of n-dodecane in detail, and the simulation
             results are in good agreement with the experimental measurements. In addition, this data set shows
             excellent transferability to different long-chain alkanes. These results demonstrate the advantages
             of the proposed method for constructing training data sets for similar systems. },
    image = {https://pubs.acs.org/na101/home/literatum/publisher/achs/journals/content/enfuem/2021/enfuem.2021.35.issue-1/acs.energyfuels.0c03211/20201230/images/medium/ef0c03211_0006.gif},
    googlescholar = 2272514516724855984,
    researchgate = 347440142,
    pdf = {https://scholarship.libraries.rutgers.edu/view/pdfCoverPage?instCode=01RUT_INST&filePid=13678688170004646&download=true}
}

@article{Zhang_ComputPhysCommun_2020_v253_p107206,
    title = {{DP-GEN: A concurrent learning platform for the generation of
            reliable deep learning based potential energy models}},
    journal = {Computer Physics Communnications},
    pages = 107206,
    year = 2020,
    volume = 253,
    issn = {0010-4655},
    doi = {10.1016/j.cpc.2020.107206},
    url = {http://www.sciencedirect.com/science/article/pii/S001046552030045X},
    author = {Yuzhi Zhang and Haidi Wang and Weijie Chen and Jinzhe Zeng and Linfeng Zhang* and Han Wang* and Weinan E*},
    keywords = {Many-body potential energy, Deep learning, Concurrent learning},
    abstract = {In recent years, promising deep learning based interatomic potential
                energy surface (PES) models have been proposed that can potentially
                allow us to perform molecular dynamics simulations for large scale
                systems with quantum accuracy. However, making these models truly
                reliable and practically useful is still a very non-trivial task. A
                key component in this task is the generation of datasets used in model
                training. In this paper, we introduce the Deep Potential GENerator
                (DP-GEN), an open-source software platform that implements the recently
                proposed on-the-fly learning procedure (Zhang et al. 2019) and is
                capable of generating uniformly accurate deep learning based PES models
                in a way that minimizes human intervention and the computational cost for
                data generation and model training. DP-GEN automatically and iteratively
                performs three steps: exploration, labeling, and training. It supports
                various popular packages for these three steps: LAMMPS for exploration,
                Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for
                training. It also allows automatic job submission and result collection
                on different types of machines, such as high performance clusters and
                cloud machines, and is adaptive to different job management tools,
                including Slurm, PBS, and LSF. As a concrete example, we illustrate the
                details of the process for generating a general-purpose PES model for Cu
                using DP-GEN.
                Program summary
                Program Title: DP-GEN
                Program Files doi: http://dx.doi.org/10.17632/sxybkgc5xc.1
                Licensing provisions: LGPL
                Programming language: Python
                Nature of problem: Generating reliable deep learning based potential energy
                models with minimal human intervention and computational cost.
                Solution method: The concurrent learning scheme is implemented. Supports for
                sampling configuration space with LAMMPS, generating ab initio data with
                Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit                                                                                               are provided. Supports for different machines including workstations, high
                performance clusters and cloud machines are provided. Supports for job
                management tools including Slurm, PBS, LSF are provided.},
    image = {https://ars.els-cdn.com/content/image/1-s2.0-S001046552030045X-gr1.jpg},
    googlescholar = 2414728760719101194,
    github = {deepmodeling/dpgen},
    researchgate = 339191433,
    pdf = {https://arxiv.org/pdf/1910.12690}
}

@article{Han_ACSAppliedMatInterface_2018_v10_p31725,
  author = {Xin-Yue Han and Zi-Han Chen and Jin-Zhe Zeng and Qian-Xi Fan and Zheng-Qi Fang and Guoyue Shi and Min Zhang*},
  title = {Inorganic-Organic Hybrid Tongue-Mimic for Time-Resolved Luminescent Noninvasive 
           Pattern and Chiral Recognition of Thiols in Biofluids toward Healthcare Monitoring},
  journal = {ACS Applied Materials & Interfaces},
  volume = 10,
  number = 37,
  pages = {31725--31734},
  year = 2018,
  doi = {10.1021/acsami.8b13498},
  note ={PMID: 30148952},
  pdf = {http://pubs.acs.org/articlesonrequest/AOR-vrge2mi5rK4SqzyuasNV},
  URL = { 
      https://doi.org/10.1021/acsami.8b13498
  },
  eprint = { 
      https://doi.org/10.1021/acsami.8b13498
  },
  abstract = { In this work, manganese(II)-doped zinc/germanium oxide nanoparticles (Mn@ZGNPs) 
         have been hydrothermally synthesized to equip with appealing time-resolved luminescence (TRL). 
         Interestingly, we reveal that they can be readily quenched (“turn off”) via a facile surface 
         coating with bioinspired polydopamine (PDA) polymerized from dopamine (DA), resulting from 
         PDA-triggered TRL resonance energy transfer (TRL-RET). By integrated with the thiol-induced 
         inhibition of PDA formation, an ingenious inorganic–organic hybrid tongue-mimic sensor array is 
         thus unveiled for noninvasive pattern recognition of thiols in biofluids in a TRL-RET-reversed 
         “turn on” format toward healthcare monitoring. The sensing principle is based on the new finding 
         that there are differential inhibitions from thiols against the polymerization of DA with 
         various concentrations. Furthermore, density function theory (DFT) studies excellently prove our 
         sensing principle and experimental results, reinforcing the power of the presented system. More 
         importantly, chiral recognition of varied concentrations and mixtures of cysteine enantiomers 
         using our platform are also been demonstrated, promising its practical usage. This is a novel 
         concept of inorganic–organic hybrid-based pattern and chiral recognition platform for TRL 
         background-free sensing and would sprout more novel relevant strategies toward broader 
         applications. },
  image = {https://pubs.acs.org/na101/home/literatum/publisher/achs/journals/content/aamick/2018/aamick.2018.10.issue-37/acsami.8b13498/20180913/images/medium/am-2018-134983_0007.gif},
  googlescholar = 2654767107500018882,
  researchgate = 327266394
}

@article{Tian_PhysChemChemPhys_2019_v21_p22103,
  title={Understanding the selectivity of inhibitors toward PI4KIII$\alpha$ and PI4KIII$\beta$ based molecular modeling},
  author={Shuaizhen Tian and Jinzhe Zeng and Xiao Liu* and Jianzhong Chen and John ZH Zhang and Tong Zhu*},
  journal={Physical Chemistry Chemical Physics},
  volume=21,
  number=39,
  pages={22103--22112},
  year=2019,
  publisher={Royal Society of Chemistry},
  doi={10.1039/C9CP03598B},
  googlescholar = 13761803133090538045,
  researchgate = 335902154,
  pdf = {https://scholarship.libraries.rutgers.edu/view/pdfCoverPage?instCode=01RUT_INST&filePid=13678734800004646&download=true}
}

@article{Cao_ChemRxiv_2019,
  title={Fragment-based Ab Initio Molecular Dynamics Simulation for Combustion},
  author={Liqun Cao and Jinzhe Zeng and Mingyuan Xu and Chih-Hao Chin and Tong Zhu* and John ZH Zhang*},
  year=2021,
  journal={Molecules},
  volume=26,
  number=11,
  pages=3120,
  doi={10.3390/molecules26113120},
  pdf={https://www.mdpi.com/1420-3049/26/11/3120/pdf},
  googlescholar=8805505848779860219,
  image={https://www.mdpi.com/molecules/molecules-26-03120/article_deploy/html/images/molecules-26-03120-g001-550.jpg},
  github={tongzhugroup/aimdfragmentation},
  researchgate=351820137
}

@article{Zeng_ChemRxiv_2021,
  title={{Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution}},
  author={Jinzhe Zeng and Timothy J. Giese and ̧Sölen Ekesan and Darrin M. York*},
  year=2021,
  volume=17,
  number=11,
  pages={6993--7009},
  journal={Journal of Chemical Theory and Computation},
  doi={10.1021/acs.jctc.1c00201},
  pdf={https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7557dbdbb899a20a3a828/original/development-of-range-corrected-deep-learning-potentials-for-fast-accurate-quantum-mechanical-molecular-mechanical-simulations-of-chemical-reactions-in-solution.pdf},
  googlescholar = 1262175988288864992,
  researchgate = 355227015,
  image={https://pubs.acs.org/na101/home/literatum/publisher/achs/journals/content/jctcce/2021/jctcce.2021.17.issue-11/acs.jctc.1c00201/20211102/images/medium/ct1c00201_0008.gif}
}

@article{Cao_PhysChemChemPhys_2022,
  title={{Ab Initio Neural Network MD Simulation of Thermal Decomposition of High Energy Material CL-20/TNT}},
  author={Liqun Cao and Jinzhe Zeng and Bo Wang and Tong Zhu* and John Z.H. Zhang*},
  year=2022,
  journal={Physical Chemistry Chemical Physics},
  doi={10.1039/D2CP00710J},
  volume=24,
  issue=19,
  pages={11801--11811},
  researchgate = 360018447,
}

@article{Giese_2022_JChemTheoryComput,
author = {Giese, Timothy J. and Zeng, Jinzhe and Ekesan, Şölen and Darrin M. York*},
title = {Combined QM/MM, Machine Learning Path Integral Approach to Compute Free Energy Profiles and Kinetic Isotope Effects in RNA Cleavage Reactions},
journal = {Journal of Chemical Theory and Computation},
year = {2022},
doi = {10.1021/acs.jctc.2c00151},
volume=18,
issue=7,
pages={4304--4317},
    note ={PMID: 35709391},
    abstract = { We present a fast, accurate, and robust approach for determination of free energy profiles and kinetic isotope effects for RNA 2′-O-transphosphorylation reactions with inclusion of nuclear quantum effects. We apply a deep potential range correction (DPRc) for combined quantum mechanical/molecular mechanical (QM/MM) simulations of reactions in the condensed phase. The method uses the second-order density-functional tight-binding method (DFTB2) as a fast, approximate base QM model. The DPRc model modifies the DFTB2 QM interactions and applies short-range corrections to the QM/MM interactions to reproduce ab initio DFT (PBE0/6-31G*) QM/MM energies and forces. The DPRc thus enables both QM and QM/MM interactions to be tuned to high accuracy, and the QM/MM corrections are designed to smoothly vanish at a specified cutoff boundary (6 Å in the present work). The computational speed-up afforded by the QM/MM+DPRc model enables free energy profiles to be calculated that include rigorous long-range QM/MM interactions under periodic boundary conditions and nuclear quantum effects through a path integral approach using a new interface between the AMBER and i-PI software. The approach is demonstrated through the calculation of free energy profiles of a native RNA cleavage model reaction and reactions involving thio-substitutions, which are important experimental probes of the mechanism. The DFTB2+DPRc QM/MM free energy surfaces agree very closely with the PBE0/6-31G* QM/MM results, and it is vastly superior to the DFTB2 QM/MM surfaces with and without weighted thermodynamic perturbation corrections. 18O and 34S primary kinetic isotope effects are compared, and the influence of nuclear quantum effects on the free energy profiles is examined. },
  image={https://pubs.acs.org/cms/10.1021/acs.jctc.2c00151/asset/images/medium/ct2c00151_0006.gif},
  researchgate=361379553,
}

@incollection{Zeng_2022_Chapter,
  title = {Chapter 12 - Neural network potentials},
  editor = {Pavlo O. Dral},
  booktitle = {Quantum Chemistry in the Age of Machine Learning},
  publisher = {Elsevier},
  pages = {279-294},
  year = {2023},
  isbn = {978-0-323-90049-2},
  doi = {10.1016/B978-0-323-90049-2.00001-9},
  author = {Jinzhe Zeng and Liqun Cao and Tong Zhu*},
  keywords = {Neural network, Potential energy surface, Molecular dynamic simulation, Chemical reaction},
  abstract = {Recently, artificial neural network-based methods for the construction of potential energy surfaces and molecular dynamics (MD) simulations based on them have been increasingly used in the field of theoretical chemistry. The neural network potentials (NNP) strike a good balance between accuracy and computational efficiency relative to quantum chemical calculations and MD simulations based on classical force fields. Thus, NNP is becoming a powerful tool for studying the structure and function of molecules. In this chapter, we introduce the basic theory of NNP. The construction steps and the usage of NNP are also introduced in detail with the MD simulation of methane combustion as an example. We hope that this chapter can help those readers who are new but interested in entering this field.},
  image = {https://ars.els-cdn.com/content/image/3-s2.0-B9780323900492000019-f12-01-9780323900492.sml},
  github = {tongzhugroup/Chapter13-tutorial},
  researchgate = 363856260,
}

@Article{Giese_JPhysChemA_2022_vNone_pNone,
    author =   {Timothy J Giese and Jinzhe Zeng and Darrin M York},
    title =    {{Multireference Generalization of the Weighted Thermodynamic
             Perturbation Method}},
    journal =  {J. Phys. Chem. A},
    year =     2022,
    volumn = 126,
    issue = 45,
    pages = {8519--8533},
    annote =   {We describe the generalized weighted thermodynamic perturbation (gwTP)
             method for estimating the free energy surface of an expensive "high-
             level" potential energy function from the umbrella sampling performed
             with multiple inexpensive "low-level" reference potentials. The gwTP
             method is a generalization of the weighted thermodynamic perturbation
             (wTP) method developed by Li and co-workers [J. Chem. Theory Comput.
             2018, 14, 5583-5596] that uses a single "low-level" reference
             potential. The gwTP method offers new possibilities in model design
             whereby the sampling generated from several low-level potentials may
             be combined (e.g., specific reaction parameter models that might have
             variable accuracy at different stages of a multistep reaction). The
             gwTP method is especially well suited for use with machine learning
             potentials (MLPs) that are trained against computationally expensive
             ab initio quantum mechanical/molecular mechanical (QM/MM) energies and
             forces using active learning procedures that naturally produce
             multiple distinct neural network potentials. Simulations can be
             performed with greater sampling using the fast MLPs and then corrected
             to the ab initio level using gwTP. The capabilities of the gwTP method
             are demonstrated by creating reference potentials based on the MNDO/d
             and DFTB2/MIO semiempirical models supplemented with the "range-
             corrected deep potential" (DPRc). The DPRc parameters are trained to
             ab initio QM/MM data, and the potentials are used to calculate the
             free energy surface of stepwise mechanisms for nonenzymatic RNA
             2'-O-transesterification model reactions. The extended sampling made
             possible by the reference potentials allows one to identify
             unequilibrated portions of the simulations that are not always evident
             from the short time scale commonly used with ab initio QM/MM
             potentials. We show that the reference potential approach can yield
             more accurate ab initio free energy predictions than the wTP method or
             what can be reasonably afforded from explicit ab initio QM/MM
             sampling.},
    doi =      {10.1021/acs.jpca.2c06201},
	researchgate = 364824210,
}

@Article{Bai_ChemCommun_2021_v57_p11633,
    author =   {Jiawei Bai and Xingchen Liu and Tingyu Lei and Botao Teng and Xiaodong
             Wen},
    title =    {{A combined DFTB nanoreactor and reaction network generator approach
             for the mechanism of hydrocarbon combustion}},
    journal =  {Chem. Commun. (Camb).},
    year =     2021,
    volume =   57,
    issue =    88,
    pages =    {11633--11636},
    annote =   {We explored the mechanism of ethylene combustion by combining a
             density functional tight-binding based nanoreactor molecular dynamic
             method (DFTB-NMD) and a hidden Markov model (HMM) based reaction
             network generator approach. The results demonstrate that the DFTB-NMD
             is a promising method to predict the mechanism of complicated
             combustion reactions.},
    doi =      {10.1039/d1cc04736a},
}

@Article{Li_InternationalJournalofHydrogenEnergy_2021_v46_p38854,
    author =   {Wenjuan Li and Shuo Yu and Liang Zhang and Jianfa Chen and Weiguo Cao
             and Yanhua Lan},
    title =    {{ReaxFF molecular dynamics simulations of n-eicosane reaction
             mechanisms during pyrolysis and combustion}},
    journal =  {International Journal of Hydrogen Energy},
    year =     2021,
    volume =   46,
    issue =    78,
    pages =    {38854--38870},
    doi =      {10.1016/j.ijhydene.2021.08.234},
}

@Article{Lei_JPhysChemLett_2021_v12_p9413,
    author =   {Tingyu Lei and Xingchen Liu and Amar Deep Pathak and Sharan Shetty and
             Qingya Liu and Xiaodong Wen},
    title =    {{Insights into Coke Formation and Removal under Operating Conditions
             with a Quantum Nanoreactor Approach}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2021,
    volume =   12,
    issue =    39,
    pages =    {9413--9421},
    annote =   {The in situ formation and removal of coke is a critical problem in
             heterogeneous catalysis, but its mechanism is not well understood.
             This work investigates the mechanism of carbon deposition and
             hydrogenation on an Fe cluster under high-temperature conditions with
             the density functional tight-binding (DFTB) based nanoreactor
             molecular dynamics (NMD) method. Our study shows that successive
             formation of carbon chains, rings, and fused rings occurred during the
             carbon deposition on Fe clusters. Hydrogenation of activated carbon
             happens through direct C-H coupling, while the hydrogenation of
             graphitic carbon involves hydrogenation of the edge carbon, ring-
             opening reaction, and dealkylation reaction. The main function of the
             Fe catalyst is to provide the active sites for H2 dissociation and
             dissociated H spillover, while its activity toward C-C bond breaking
             is limited. These results highlight the role of the DFTB-NMD method as
             an effective tool to investigate reaction mechanisms under operating
             conditions in heterogeneous catalysis.},
    doi =      {10.1021/acs.jpclett.1c02892},
}

@Article{Ponnuchamy_Processes_2021_v9_p714,
    author =   {Veerapandian Ponnuchamy and Jakub Sandak and Anna Sandak},
    title =    {{Revealing of Supercritical Water Gasification Process of Lignin by
             Reactive Force Field Molecular Dynamics Simulations}},
    journal =  {Processes},
    year =     2021,
    volume =   9,
    issue =    4,
    pages =    714,
    annote =   {<jats:p>Gasification with supercritical water is an efficient process
             that can be used for the valorization of biomass. Lignin is the second
             most abundant biopolymer in biomass and its conversion is fundamental
             for future energy and value-added chemicals. In this paper, the
             supercritical water gasification process of lignin by employing
             reactive force field molecular dynamics simulations (ReaxFF MD) was
             investigated. Guaiacyl glycerol-{\ensuremath{\beta}}-guaiacyl ether
             (GGE) was considered as a lignin model to evaluate the reaction
             mechanism and identify the components at different temperatures from
             1000 K to 5000 K. The obtained results revealed that the reactions and
             breaking of the lignin model started at 2000 K. At the primary stage
             of the reaction at 2000 K the {\ensuremath{\beta}}-O-4 bond tends to
             break into several compounds, forming mainly guaiacol and
             1,3-benzodioxole. In particular, 1,3-benzodioxole undergoes
             dissociation and forms cyclopentene-based ketones. Afterward,
             dealkylation reaction occurred through hydroxyl radicals of water to
             form methanol, formaldehyde and methane. Above 2500 K, H2, CO and CO2
             are predominantly formed in which water molecules contributed hydrogen
             and oxygen for their formation. Understanding the detailed reactive
             mechanism of lignin{\textquoteright}s gasification is important for
             efficient energy conversion of biomass.</jats:p>},
    doi =      {10.3390/pr9040714},
}

@Article{Wang_MaterialsTodayCommunications_2022_v33_p104379,
    author =   {Hao-Ran Wang and Chong Zhang and Cheng-Guo Sun and Bing-Cheng Hu and
             Xue-Hai Ju},
    title =    {{5,6-Biheterocyclic pentazolate salts as promising energetic materials:
             a new design strategy}},
    journal =  {Materials Today Communications},
    year =     2022,
    volume =   33,
    pages =    104379,
    doi =      {10.1016/j.mtcomm.2022.104379},
}

@Article{Wang_JACSAu_2022_v2_p2081,
    author =   {Yutong Wang and Guozhu Liu},
    title =    {{Inhomogeneity Effects on Reactions in Supercritical Fluids: A
             Computational Study on the Pyrolysis of <i>n</i>-Decane}},
    journal =  {JACS Au},
    year =     2022,
    volume =   2,
    issue =    9,
    pages =    {2081--2088},
    annote =   {Supercritical fluids exhibit peculiar inhomogeneity, which strongly
             affects reaction behaviors in them. However, explanations for
             inhomogeneity and its effect on reactions are both ambiguous so far.
             Here, we provide an atomic-level understanding of inhomogeneity
             effects on reactions via the computational method, with the example of
             n-decane pyrolysis under supercritical conditions. We describe the
             characteristic pyrolysis behaviors through collective variable-driven
             hyperdynamics (CVHD) simulations and explain the inhomogeneity of
             supercritical n-decane as the coexistence of gas-like and liquid-like
             atoms by a trained machine learning classifier. Due to their specific
             local environment, the appearance of liquid-like atoms under
             supercritical conditions significantly increases the type and
             frequency of bimolecular reactions and eventually causes changes in
             product distributions. Future research with this method is expected to
             extend the effect of inhomogeneity on other reactions under
             supercritical conditions or other condensed phases.},
    PMCID =    {PMC9516705},
    doi =      {10.1021/jacsau.2c00359},
}

@Article{Chen_ChemicalEngineeringJournal_2023_v451_p138644,
    author =   {Jingwei Chen and Yu Bai and Tian Meng and Qiteng Wang and Chenxi Wang
             and E. Jiaqiang},
    title =    {{Detailed mechanisms of amoxicillin decomposition in supercritical
             water by ReaxFF reactive molecular dynamics simulation}},
    journal =  {Chemical Engineering Journal},
    year =     2023,
    volume =   451,
    pages =    138644,
    doi =      {10.1016/j.cej.2022.138644},
}

@Article{Batuer_ChemicalEngineeringJournal_2021_v405_p126633,
    author =   {Adili Batuer and Dezhen Chen and Xingchu He and Zhen Huang},
    title =    {{Simulation methods of cotton pyrolysis based on ReaxFF and the
             influence of volatile removal ratio on volatile evolution and char
             formation}},
    journal =  {Chemical Engineering Journal},
    year =     2021,
    volume =   405,
    pages =    126633,
    doi =      {10.1016/j.cej.2020.126633},
}

@Article{Chu_PhysChemChemPhys_2022_vNone_pNone,
    author =   {Qingzhao Chu and Xiaoya Chang and Kang Ma and Xiaolong Fu and Dongping
             Chen},
    title =    {{Revealing the thermal decomposition mechanism of RDX crystals by a
             neural network potential}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2022,
    annote =   {<jats:p>A neural network potential (NNP) is developed to investigate
             the complex reaction dynamics of 1,3,5-trinitro-1,3,5-triazine (RDX)
             thermal decomposition.</jats:p>},
    doi =      {10.1039/D2CP03511A},
}
@Article{Chu_JPhysChemLett_2022_v13_p4052,
    author =   {Qingzhao Chu and Kai H Luo and Dongping Chen},
    title =    {{Exploring Complex Reaction Networks Using Neural Network-Based
             Molecular Dynamics Simulation}},
    journal =  {J. Phys. Chem. Lett.},
    year =     2022,
    volume =   13,
    issue =    18,
    pages =    {4052--4057},
    annote =   {Ab initio molecular dynamics (AIMD) is an established method for
             revealing the reactive dynamics of complex systems. However, the high
             computational cost of AIMD restricts the explorable length and time
             scales. Here, we develop a fundamentally different approach using
             molecular dynamics simulations powered by a neural network potential
             to investigate complex reaction networks. This potential is trained
             via a workflow combining AIMD and interactive molecular dynamics in
             virtual reality to accelerate the sampling of rare reactive processes.
             A panoramic visualization of the complex reaction networks for
             decomposition of a novel high explosive (ICM-102) is achieved without
             any predefined reaction coordinates. The study leads to the discovery
             of new pathways that would be difficult to uncover if established
             methods were employed. These results highlight the power of neural
             network-based molecular dynamics simulations in exploring complex
             reaction mechanisms under extreme conditions at the ab initio level,
             pushing the limit of theoretical and computational chemistry toward
             the realism and fidelity of experiments.},
    doi =      {10.1021/acs.jpclett.2c00647},
}

@Article{Liu_InternationalJournalofHydrogenEnergy_2022_vNone_pNone,
    author =   {Xiuting Liu and Min Zhao and Muye Feng and Yuejin Zhu},
    title =    {{Study on mechanisms of methane/hydrogen blended combustion using
             reactive molecular dynamics simulation}},
    journal =  {International Journal of Hydrogen Energy},
    year =     2022,
    doi =      {10.1016/j.ijhydene.2022.10.050},
}

@Article{Zhu_Processes_2022_v10_p2378,
    author =   {Qiren Zhu and Fang Wang and Jie-Yao Lyu and Yang Li and Dongping Chen
             and Wenming Yang},
    title =    {{Molecular Dynamics Simulation on the Pyrolysis Process of PODE3-5}},
    journal =  {Processes},
    year =     2022,
    volume =   10,
    issue =    11,
    pages =    2378,
    annote =   {<jats:p>This paper investigates the pyrolysis of PODEn (n = 3, 4, 5)
             using ReaxFF molecular dynamics simulation. A large-scale model, which
             contains 2000 PODEn molecules, is simulated at 3000 K. The higher
             frequencies of the initial PODEn decomposition reaction at
             {\ensuremath{\alpha}} or {\ensuremath{\beta}} C-O bond show that the
             {\ensuremath{\alpha}} or {\ensuremath{\beta}} C-O bond in PODEn is not
             easy to break, which is approximately half the number of the other
             type of C-O bond dissociation. Furthermore, the bond dissociation
             energies (BDEs) are calculated using the ReaxFF method. The BDE of
             {\ensuremath{\alpha}} or {\ensuremath{\beta}} C-O bond is higher than
             that of the other C-O bond, ~3{\textendash}11 kcal/mol, indicating
             that BDE is one of the factors causing the different proportions of
             bonds broken. The evolution of pyrolysis products is also
             investigated. The results reveal that long-chain pyrolysis products
             from the initial PODEn decomposition are prone to further reaction,
             while a large amount of CH3O and CH3 remains in the system. This helps
             explain the difficulty in {\ensuremath{\alpha}} and
             {\ensuremath{\beta}} C-O bond dissociation reactions. The results of
             the pyrolysis products are consistent with the result in further
             chemical kinetic simulation. The C2 species in pyrolysis products is
             relatively low, especially for C2H4 and C2H3, which is around zero.
             This supports the ability of PODEn to reduce soot emission.</jats:p>},
    doi =      {10.3390/pr10112378},
}

@Article{She_Molecules_2022_v27_p8255,
    author =   {Chongchong She and Kun Chen and Minglei Chen and Zhiyan Lu and Nana Wu
             and Lijie Li and Junfeng Wang and Shaohua Jin},
    title =    {{Coupling Effect of Non-Ignition Impact and Heat on the Decay of FOX-7}},
    journal =  {Molecules},
    year =     2022,
    volume =   27,
    issue =    23,
    pages =    8255,
    annote =   {<jats:p>Non-ignition impact and heat stimuli are the most common
             external stimuli loaded on energetic materials. Nevertheless, there is
             thereby an urgent need, but it is still a significant challenge to
             comprehend their coupling effects on the decay and safety mechanisms
             of energetic materials. Then, reactive molecular dynamics simulation
             was employed to mimic practical situations and reveal the impact heat
             coupling effect on the decay mechanism of FOX-7. The temperature and
             the degree of compression of the crystal caused by the impact are
             considered variables in the simulation. Both increasing the degree of
             compression and elevating the temperature promotes the decay of FOX-7.
             However, their underlying response mechanism is not the same. The
             acceleration of decomposition is due to the elevated potential energy
             of the FOX-7 molecules because of elevating the temperature. In
             addition to the elevated potential energy of the molecule, the main
             contribution to the decomposition from the compression is to change
             the decomposition path. The results of the analysis show that
             compression reduces the stability of the C=C bond, so that chemical
             reactions related to the double bond occur. In addition,
             interestingly, the compression along the c direction has an almost
             equal effect on the final product as the compression along the b
             direction. Finally, the decay reaction networks are proposed to
             provide insights into the decomposition mechanism on atomic level. All
             these findings are expected to pave a way to understand the underlying
             response mechanism for the FOX-7 against external stimuli.</jats:p>},
    doi =      {10.3390/molecules27238255},
}

@Article{Zeng_JChemTheoryComput_2023_vNone_pNone,
    author =   {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York},
    title =    {{QD{\ensuremath{\pi}}: A Quantum Deep Potential Interaction Model for
             Drug Discovery}},
    journal =  {J. Chem. Theory Comput.},
    year =     2023,
    annote =   {We report QD{\ensuremath{\pi}}-v1.0 for modeling the internal energy
             of drug molecules containing H, C, N, and O atoms. The
             QD{\ensuremath{\pi}} model is in the form of a quantum
             mechanical/machine learning potential correction
             (QM/{\ensuremath{\Delta}}-MLP) that uses a fast third-order self-
             consistent density-functional tight-binding (DFTB3/3OB) model that is
             corrected to a quantitatively high-level of accuracy through a deep-
             learning potential (DeepPot-SE). The model has the advantage that it
             is able to properly treat electrostatic interactions and handle
             changes in charge/protonation states. The model is trained against
             reference data computed at the {\ensuremath{\omega}}B97X/6-31G* level
             (as in the ANI-1x data set) and compared to several other approximate
             semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3,
             MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QD{\ensuremath{\pi}}
             model is demonstrated to be accurate for a wide range of intra- and
             intermolecular interactions (despite its intended use as an internal
             energy model) and has shown to perform exceptionally well for relative
             protonation/deprotonation energies and tautomers. An example
             application to model reactions involved in RNA strand cleavage
             catalyzed by protein and nucleic acid enzymes illustrates
             QD{\ensuremath{\pi}} has average errors less than 0.5 kcal/mol,
             whereas the other models compared have errors over an order of
             magnitude greater. Taken together, this makes QD{\ensuremath{\pi}}
             highly attractive as a potential force field model for drug discovery.},
    doi =      {10.1021/acs.jctc.2c01172},
    researchgate = 367433019,
}

@Article{Xu_PolymerDegradationandStability_2023_v208_p110249,
    author =   {Zhiheng Xu and Qiyuan Xie and Cheng Chen and Xi Jiang},
    title =    {{Molecular dynamics simulation of converting waste polyethylene (PE) to
             chemicals and fuels under non-isothermal and isothermal conditions}},
    journal =  {Polymer Degradation and Stability},
    year =     2023,
    volume =   208,
    pages =    110249,
    doi =      {10.1016/j.polymdegradstab.2023.110249},
}

@InCollection{Liang_BookCharp_MultiscaleModeling_2023_p6-1,
  author = {Wenshuo Liang and Jinzhe Zeng and Darrin M. York and Linfeng Zhang and Han Wang},
  title = {Learning DeePMD-Kit: A Guide to Building Deep Potential Models},
  booktitle = {A Practical Guide to Recent Advances in Multiscale Modeling and Simulation of Biomolecules},
  chapter = {Chapter 6},
  pages = {6-1--6-20},
  editor = {Yong Wang and Ruhong Zhou},
  publisher = {AIP Publishing},
  year = 2023,
  location = {Melville, New York},
  doi = {10.1063/9780735425279_006}
}

@Article{Zeng_JChemPhys_2023_v158_p124110,
    author =   {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York},
    title =    {{Modern semiempirical electronic structure methods and machine learning
             potentials for drug discovery: Conformers, tautomers, and protonation
             states}},
    journal =  {J. Chem. Phys.},
    year =     2023,
    volume =   158,
    issue =    12,
    pages =    124110,
    annote =   {Modern semiempirical electronic structure methods have considerable
             promise in drug discovery as universal "force fields" that can
             reliably model biological and drug-like molecules, including
             alternative tautomers and protonation states. Herein, we compare the
             performance of several neglect of diatomic differential overlap-based
             semiempirical (MNDO/d, AM1, PM6, PM6-D3H4X, PM7, and ODM2), density-
             functional tight-binding based (DFTB3, DFTB/ChIMES, GFN1-xTB, and
             GFN2-xTB) models with pure machine learning potentials (ANI-1x and
             ANI-2x) and hybrid quantum mechanical/machine learning potentials
             (AIQM1 and QD{\ensuremath{\pi}}) for a wide range of data computed at
             a consistent {\ensuremath{\omega}}B97X/6-31G* level of theory (as in
             the ANI-1x database). This data includes conformational energies,
             intermolecular interactions, tautomers, and protonation states.
             Additional comparisons are made to a set of natural and synthetic
             nucleic acids from the artificially expanded genetic information
             system that has important implications for the design of new
             biotechnology and therapeutics. Finally, we examine the acid/base
             chemistry relevant for RNA cleavage reactions catalyzed by small
             nucleolytic ribozymes, DNAzymes, and ribonucleases. Overall, the
             hybrid quantum mechanical/machine learning potentials appear to be the
             most robust for these datasets, and the recently developed
             QD{\ensuremath{\pi}} model performs exceptionally well, having
             especially high accuracy for tautomers and protonation states relevant
             to drug discovery.},
    PMCID =    {PMC10052497},
    doi =      {10.1063/5.0139281},
    researchgate = 369045636,
}

@article{Zeng_2023_deepmdkit,
    title  = {{DeePMD-kit v2: A software package for deep potential models}},
    author =   {Jinzhe Zeng and Duo Zhang and Denghui Lu and Pinghui Mo and Zeyu Li
             and Yixiao Chen and Mari{\'a}n Rynik and Li'ang Huang and Ziyao Li and 
             Shaochen Shi and Yingze Wang and Haotian Ye and Ping Tuo and Jiabin
             Yang and Ye Ding and Yifan Li and Davide Tisi and Qiyu Zeng and Han 
             Bao and Yu Xia and Jiameng Huang and Koki Muraoka and Yibo Wang and 
             Junhan Chang and Fengbo Yuan and Sigbj{\o}rn L{\o}land Bore and Chun
             Cai and Yinnian Lin and Bo Wang and Jiayan Xu and Jia-Xin Zhu and 
             Chenxing Luo and Yuzhi Zhang and Rhys E A Goodall and Wenshuo Liang
             and Anurag Kumar Singh and Sikai Yao and Jingchao Zhang and Renata
             Wentzcovitch and Jiequn Han and Jie Liu and Weile Jia and Darrin M
             York and Weinan E and Roberto Car and Linfeng Zhang and Han Wang},
    journal =  {J. Chem. Phys.},
    volume =   159,
    issue =    5,  
    year =    2023,
    pages  =   054801,
    annote =   {DeePMD-kit is a powerful open-source software package that facilitates
             molecular dynamics simulations using machine learning potentials known
             as Deep Potential (DP) models. This package, which was released in
             2017, has been widely used in the fields of physics, chemistry,
             biology, and material science for studying atomistic systems. The 
             current version of DeePMD-kit offers numerous advanced features, such
             as DeepPot-SE, attention-based and hybrid descriptors, the ability to
             fit tensile properties, type embedding, model deviation, DP-range
             correction, DP long range, graphics processing unit support for 
             customized operators, model compression, non-von Neumann molecular
             dynamics, and improved usability, including documentation, compiled
             binary packages, graphical user interfaces, and application
             programming interfaces. This article presents an overview of the 
             current major version of the DeePMD-kit package, highlighting its 
             features and technical details. Additionally, this article presents a
             comprehensive procedure for conducting molecular dynamics as a
             representative application, benchmarks the accuracy and efficiency of
             different models, and discusses ongoing developments.},
    doi =      {10.1063/5.0155600},

}

@article{Liu_Gaodengxuexiaohuaxuexuebao_2022_v43_p20210834,
author = {[刘嘉欣, 闵杰, 许华杰, 任海生, 谈宁馨]},
title = {基于反应力场分子模拟的乙烯燃烧自由基与氮气相互作用研究},
publisher = {高等学校化学学报},
year = {2022},
journal = {高等学校化学学报},
volume = {43},
number = {4},
eid = {20210834},
pages = {20210834-},
keywords = {;乙烯燃烧;瞬发型一氧化氮;反应力场分子动力学},
doi = http://www.cjcu.jlu.edu.cn/CN/10.7503/cjcu20210834
}

@Article{She_Molecules_2022_v27_p8255,
    author =   {Chongchong She and Kun Chen and Minglei Chen and Zhiyan Lu and Nana Wu
             and Lijie Li and Junfeng Wang and Shaohua Jin},
    title =    {{Coupling Effect of Non-Ignition Impact and Heat on the Decay of FOX-7}},
    journal =  {Molecules},
    year =     2022,
    volume =   27,
    issue =    23,
    pages =    8255,
    annote =   {Non-ignition impact and heat stimuli are the most common external
             stimuli loaded on energetic materials. Nevertheless, there is thereby
             an urgent need, but it is still a significant challenge to comprehend
             their coupling effects on the decay and safety mechanisms of energetic
             materials. Then, reactive molecular dynamics simulation was employed
             to mimic practical situations and reveal the impact heat coupling
             effect on the decay mechanism of FOX-7. The temperature and the degree
             of compression of the crystal caused by the impact are considered
             variables in the simulation. Both increasing the degree of compression
             and elevating the temperature promotes the decay of FOX-7. However,
             their underlying response mechanism is not the same. The acceleration
             of decomposition is due to the elevated potential energy of the FOX-7
             molecules because of elevating the temperature. In addition to the
             elevated potential energy of the molecule, the main contribution to
             the decomposition from the compression is to change the decomposition
             path. The results of the analysis show that compression reduces the
             stability of the C=C bond, so that chemical reactions related to the
             double bond occur. In addition, interestingly, the compression along
             the c direction has an almost equal effect on the final product as the
             compression along the b direction. Finally, the decay reaction
             networks are proposed to provide insights into the decomposition
             mechanism on atomic level. All these findings are expected to pave a
             way to understand the underlying response mechanism for the FOX-7
             against external stimuli.},
    PMCID =    {PMC9737319},
    doi =      {10.3390/molecules27238255},
}


@Article{Zhu_Processes_2022_v10_p2378,
    author =   {Qiren Zhu and Fang Wang and Jie-Yao Lyu and Yang Li and Dongping Chen
             and Wenming Yang},
    title =    {{Molecular Dynamics Simulation on the Pyrolysis Process of PODE3-5}},
    journal =  {Processes},
    year =     2022,
    volume =   10,
    issue =    11,
    pages =    2378,
    annote =   {<jats:p>This paper investigates the pyrolysis of PODEn (n = 3, 4, 5)
             using ReaxFF molecular dynamics simulation. A large-scale model, which
             contains 2000 PODEn molecules, is simulated at 3000 K. The higher
             frequencies of the initial PODEn decomposition reaction at
             {\ensuremath{\alpha}} or {\ensuremath{\beta}} C-O bond show that the
             {\ensuremath{\alpha}} or {\ensuremath{\beta}} C-O bond in PODEn is not
             easy to break, which is approximately half the number of the other
             type of C-O bond dissociation. Furthermore, the bond dissociation
             energies (BDEs) are calculated using the ReaxFF method. The BDE of
             {\ensuremath{\alpha}} or {\ensuremath{\beta}} C-O bond is higher than
             that of the other C-O bond, ~3{\textendash}11 kcal/mol, indicating
             that BDE is one of the factors causing the different proportions of
             bonds broken. The evolution of pyrolysis products is also
             investigated. The results reveal that long-chain pyrolysis products
             from the initial PODEn decomposition are prone to further reaction,
             while a large amount of CH3O and CH3 remains in the system. This helps
             explain the difficulty in {\ensuremath{\alpha}} and
             {\ensuremath{\beta}} C-O bond dissociation reactions. The results of
             the pyrolysis products are consistent with the result in further
             chemical kinetic simulation. The C2 species in pyrolysis products is
             relatively low, especially for C2H4 and C2H3, which is around zero.
             This supports the ability of PODEn to reduce soot emission.</jats:p>},
    doi =      {10.3390/pr10112378},
}


@Article{Chu_PhysChemChemPhys_2022_v24_p25885,
    author =   {Qingzhao Chu and Xiaoya Chang and Kang Ma and Xiaolong Fu and Dongping
             Chen},
    title =    {{Revealing the thermal decomposition mechanism of RDX crystals by a
             neural network potential}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2022,
    volume =   24,
    issue =    42,
    pages =    {25885--25894},
    annote =   {A neural network potential (NNP) is developed to investigate the
             complex reaction dynamics of 1,3,5-trinitro-1,3,5-triazine (RDX)
             thermal decomposition. Our NNP model is proven to possess good
             computational efficiency and retain the ab initio accuracy, which
             allows the investigation of the entire decomposition process of bulk
             RDX crystals from an atomic perspective. A series of molecular
             dynamics (MD) simulations are performed on the NNP to calculate the
             physical and chemical properties of the RDX crystal. The results show
             that the NNP can accurately describe the physical properties of RDX
             crystals, such as the cell parameters and the equation of state. The
             simulations of RDX thermal decomposition reveal that the NNP could
             capture the evolution of species at ab initio accuracy. The complex
             reaction network was established, and a reaction mechanism of RDX
             decomposition was provided. The N-N homolysis is the dominant channel,
             which cannot be observed in previous DFT studies of isolated RDX
             molecule. In addition, the H abstraction reaction by NO2 is found to
             be the critical pathway for NO and H2O formation, while the HONO
             elimination is relatively weak. The NNP gives an atomic insight into
             the complex reaction dynamics of RDX and can be extended to
             investigate the reaction mechanism of novel energetic materials.},
    doi =      {10.1039/d2cp03511a},
}


@Article{Sun_JMolModel_2023_v29_p292,
    author =   {Zi-Jian Sun and Hui Li and Weihua Zhu},
    title =    {{Reactive molecular dynamics simulations on the decomposition process
             of 1,3,5-trinitro-1,3,5-triazine crystal under high temperatures and
             pressure}},
    journal =  {J. Mol. Model.},
    year =     2023,
    volume =   29,
    issue =    9,
    pages =    292,
    annote =   {Reactive molecular dynamics simulations were performed to study the
             decomposition processes of 1,3,5-trinitro-1,3,5-triazine (RDX) crystal
             under high temperatures (2100, 2400, 2700, and 3000 K) and detonation
             pressure (34.5 GPa) and 0 GPa. It is found that the initial
             decomposition paths of RDX under different temperatures coupled with
             detonation pressure are similar, which is due to the N-NO2 bond
             breakage to release NO2. The formation rates of N2 and H2O are
             significantly affected by temperature, while those of CO2 are less
             influenced. The C atoms finally formed C clusters. As the temperature
             rises, the decomposition speeds up, indicating that the high
             temperature accelerates the decomposition. Applying pressure can
             reduce the reaction energy barrier and accelerate the decomposition.},
    doi =      {10.1007/s00894-023-05656-8},
}


@Article{Yu_ChemicalPhysics_2023_v573_p112008,
    author =   {Yin Yu and Liang Song and Jun Jiang and Feng-Qi Zhao and Si-Yu Xu and
             Xue-Hai Ju},
    title =    {{Reactive molecular dynamics simulations of multicomponent models for
             RP-3 jet fuel in combustion at supercritical conditions: A
             comprehensive mechanism study}},
    journal =  {Chemical Physics},
    year =     2023,
    volume =   573,
    pages =    112008,
    doi =      {10.1016/j.chemphys.2023.112008},
}


@Article{Lyu_CombustionandFlame_2023_v255_p112877,
    author =   {Jie-Yao Lyu and Qiren Zhu and Xin Bai and Xuan Ren and Jing Li and
             Dongping Chen and Vitaly G. Kiselev and Yang Li and Wenming Yang},
    title =    {{A detailed chemical kinetic mechanism of
             1,1-diamino-2,2-dinitroethylene (FOX-7) initial decomposition in the
             gas phase}},
    journal =  {Combustion and Flame},
    year =     2023,
    volume =   255,
    pages =    112877,
    doi =      {10.1016/j.combustflame.2023.112877},
}


@Article{Chen_Energy_2023_v278_p127900,
    author =   {Jingwei Chen and Chenxi Wang and Wenxue Shang and Yu Bai and Xiaomin
             Wu},
    title =    {{Study on the mechanisms of hydrogen production from alkali lignin
             gasification in supercritical water by ReaxFF molecular dynamics
             simulation}},
    journal =  {Energy},
    year =     2023,
    volume =   278,
    pages =    127900,
    doi =      {10.1016/j.energy.2023.127900},
}


@Article{Xing_Fuel_2023_v346_p128357,
    author =   {Zhihao Xing and Mengwei Yu and Cheng Chen and Xi Jiang},
    title =    {{A molecular investigation on the effects of OMEX addition on soot
             inception of diesel pyrolysis}},
    journal =  {Fuel},
    year =     2023,
    volume =   346,
    pages =    128357,
    doi =      {10.1016/j.fuel.2023.128357},
}


@Article{Xing_EnergyConversionandManagement_2023_v289_p117164,
    author =   {Zhihao Xing and Cheng Chen and Xi Jiang},
    title =    {{A molecular investigation on the mechanism of co-pyrolysis of ammonia
             and biodiesel surrogates}},
    journal =  {Energy Conversion and Management},
    year =     2023,
    volume =   289,
    pages =    117164,
    doi =      {10.1016/j.enconman.2023.117164},
}


@Article{Chu_JPhysChemC_2023_v127_p12976,
    author =   {Qingzhao Chu and Mingjie Wen and Xiaolong Fu and Abbas Eslami and
             Dongping Chen},
    title =    {{Reaction Network of Ammonium Perchlorate (AP) Decomposition: The
             Missing Piece from Atomic Simulations}},
    journal =  {J. Phys. Chem. C},
    year =     2023,
    volume =   127,
    issue =    27,
    pages =    {12976--12982},
    doi =      {10.1021/acs.jpcc.3c01666},
}


@Article{Zhang_Fuel_2023_v338_p127264,
    author =   {Xin Zhang and Yiming Zhao and Wenjuan Li and Yun Zhang and Siyu Xu and
             Sen Xu and Wen Zhou and Yizheng Fu and Yongan Feng and Weiguo Cao},
    title =    {{Combustion characteristics of three linear monohydric alcohols
             CH3(CH2)n-1OH (n{~}={~}16, 18, 22): Combined ignition experiments and
             molecular dynamics simulations}},
    journal =  {Fuel},
    year =     2023,
    volume =   338,
    pages =    127264,
    doi =      {10.1016/j.fuel.2022.127264},
}


@Article{Xu_PolymerDegradationandStability_2023_v208_p110249,
    author =   {Zhiheng Xu and Qiyuan Xie and Cheng Chen and Xi Jiang},
    title =    {{Molecular dynamics simulation of converting waste polyethylene (PE) to
             chemicals and fuels under non-isothermal and isothermal conditions}},
    journal =  {Polymer Degradation and Stability},
    year =     2023,
    volume =   208,
    pages =    110249,
    doi =      {10.1016/j.polymdegradstab.2023.110249},
}


@Article{Liu_InternationalJournalofHydrogenEnergy_2023_v48_p1625,
    author =   {Xiuting Liu and Min Zhao and Muye Feng and Yuejin Zhu},
    title =    {{Study on mechanisms of methane/hydrogen blended combustion using
             reactive molecular dynamics simulation}},
    journal =  {International Journal of Hydrogen Energy},
    year =     2023,
    volume =   48,
    issue =    4,
    pages =    {1625--1635},
    doi =      {10.1016/j.ijhydene.2022.10.050},
}


@Article{Shang_Fuel_2024_v357_p129909,
    author =   {Zhe Shang and Hui Li},
    title =    {{Unraveling pyrolysis mechanisms of lignin dimer model compounds:
             Neural network-based molecular dynamics simulation investigations}},
    journal =  {Fuel},
    year =     2024,
    volume =   357,
    pages =    129909,
    doi =      {10.1016/j.fuel.2023.129909},
}

@Article{Tao_JChemPhys_2024_v160_p224104,
    author =   {Yujun Tao and Timothy J. Giese and {\c{S}}{\"o}len Ekesan and Jinzhe
             Zeng and B{\'a}lint Aradi and Ben Hourahine and Hasan Metin Aktulga
             and Andreas W. G{\"o}tz and Kenneth M. {Merz Jr} and Darrin M. York*},
    title =    {{Amber free energy tools: Interoperable software for free energy
             simulations using generalized quantum mechanical/molecular mechanical
             and machine learning potentials}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   22,
    pages =    224104,
    doi =      {10.1063/5.0211276},
    abstract = {We report the development and testing of new integrated
             cyberinfrastructure for performing free energy simulations with
             generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and
             machine learning potentials (MLPs) in Amber. The Sander molecular
             dynamics program has been extended to leverage fast, density-
             functional tight-binding models implemented in the DFTB+ and xTB
             packages, and an interface to the DeePMD-kit software enables the use
             of MLPs. The software is integrated through application program
             interfaces that circumvent the need to perform "system calls" and
             enable the incorporation of long-range Ewald electrostatics into the
             external software's self-consistent field procedure. The
             infrastructure provides access to QM/MM models that may serve as the
             foundation for QM/MM-{\ensuremath{\Delta}}MLP potentials, which
             supplement the semiempirical QM/MM model with a MLP correction trained
             to reproduce ab{~}initio QM/MM energies and forces. Efficient
             optimization of minimum free energy pathways is enabled through a new
             surface-accelerated finite-temperature string method implemented in
             the FE-ToolKit package. Furthermore, we interfaced Sander with the
             i-PI software by implementing the socket communication protocol used
             in the i-PI client-server model. The new interface with i-PI allows
             for the treatment of nuclear quantum effects with semiempirical
             QM/MM-{\ensuremath{\Delta}}MLP models. The modular interoperable
             software is demonstrated on proton transfer reactions in guanine-
             thymine mispairs in a B-form deoxyribonucleic acid helix. The current
             work represents a considerable advance in the development of modular
             software for performing free energy simulations of chemical reactions
             that are important in a wide range of applications.},
}

@Article{Giese_JPhysChemB_2024_v128_p6257,
    author =   {Timothy J. Giese and Jinzhe Zeng and Lauren Lerew and Erika McCarthy
             and Yujun Tao and {\c{S}}{\"o}len Ekesan and Darrin M. York},
    title =    {{Software Infrastructure for Next-Generation
             QM/MM-$\Delta$MLP Force Fields}},
    journal =  {J. Phys. Chem., B},
    year =     2024,
    volume =   128,
    number =   26,
    pages =    {6257--6271},
    doi =      {10.1021/acs.jpcb.4c01466},
    abstract = {We present software infrastructure for the design and testing of new
             quantum mechanical/molecular mechanical and machine-learning potential
             (QM/MM-{\ensuremath{\Delta}}MLP) force fields for a wide range of
             applications. The software integrates Amber's molecular dynamics
             simulation capabilities with fast, approximate quantum models in the
             xtb package and machine-learning potential corrections in DeePMD-kit.
             The xtb package implements the recently developed density-functional
             tight-binding QM models with multipolar electrostatics and density-
             dependent dispersion (GFN2-xTB), and the interface with Amber enables
             their use in periodic boundary QM/MM simulations with linear-scaling
             QM/MM particle-mesh Ewald electrostatics. The accuracy of the
             semiempirical models is enhanced by including machine-learning
             correction potentials ({\ensuremath{\Delta}}MLPs) enabled through an
             interface with the DeePMD-kit software. The goal of this paper is to
             present and validate the implementation of this software
             infrastructure in molecular dynamics and free energy simulations. The
             utility of the new infrastructure is demonstrated in proof-of-concept
             example applications. The software elements presented here are open
             source and freely available. Their interface provides a powerful
             enabling technology for the design of new
             QM/MM-{\ensuremath{\Delta}}MLP models for studying a wide range of
             problems, including biomolecular reactivity and protein-ligand
             binding.},
}

@Article{Ren_Fuel_2024_v375_p132623,
    author =   {Xuan Ren and Ruining He and Xinhui Wang and Fang Wang and Xinpeng
             Zhang and Dingcheng Wang and Shuyuan Liu and Henry J. Curran and Jinhu
             Liang and Yang Li},
    title =    {{A comprehensive experimental and theoretical study of thermal response
             mechanisms of TKX-50 and HMX}},
    journal =  {Fuel},
    year =     2024,
    volume =   375,
    pages =    132623,
    doi =      {10.1016/j.fuel.2024.132623},
}

@Article{Song_Fuel_2024_v375_p132603,
    author =   {Liang Song and Tian-Cheng Zhang and Yong Zhang and Bo-Cong Chen and
             Jing Ye and Fang-Chao Hou and Jing Sun and Su-Qin Zhou},
    title =    {{Thermal decomposition behaviors of adamantane and 1-methyladamantane
             in oxygen atmosphere: ReaxFF molecular dynamics simulation}},
    journal =  {Fuel},
    year =     2024,
    volume =   375,
    pages =    132603,
    doi =      {10.1016/j.fuel.2024.132603},
}

@Article{Yu_ChemEngJ_2024_v498_p155320,
    author =   {Xiaozhen Yu and Xiangbao Meng and Jihe Chen and Yujian Zhu and Yadi Li
             and Zhao Qin and Jianxu Ding and Shizemin Song},
    title =    {{Macroscopic behavior and kinetic mechanism of ammonium dihydrogen
             phosphate for suppressing polyethylene dust deflagration}},
    journal =  {Chem. Eng. J.},
    year =     2024,
    volume =   498,
    pages =    155320,
    doi =      {10.1016/j.cej.2024.155320},
}

@Article{Hassanloo_Fuel_2024_v374_p132486,
    author =   {Hamidreza Hassanloo and Xinyan Wang},
    title =    {{Combustion mechanism of nanobubbled dodecane: A reactive molecular
             study}},
    journal =  {Fuel},
    year =     2024,
    volume =   374,
    pages =    132486,
    doi =      {10.1016/j.fuel.2024.132486},
}

@Article{Wang_Fuel_2024_v374_p132418,
    author =   {Yutong Wang and Junhao Guo and Guozhu Liu},
    title =    {{Molecular simulation on inhomogeneous microaggregation and pyrolysis
             mechanics of supercritical methylcyclohexane}},
    journal =  {Fuel},
    year =     2024,
    volume =   374,
    pages =    132418,
    doi =      {10.1016/j.fuel.2024.132418},
}

@Article{Zhang_JHazardMater_2024_v478_p135488,
    author =   {Peng Zhang and Xinbao Xu and Xiaoming Luo},
    title =    {{Degradation pathways and product formation mechanisms of asphaltene in
             supercritical water}},
    journal =  {J. Hazard. Mater.},
    year =     2024,
    volume =   478,
    pages =    135488,
    doi =      {10.1016/j.jhazmat.2024.135488},
    abstract = {Asphaltene is the compound with the most complex structure and the
             most difficult degradation in oily sludge, which is the key to limit
             the efficiency of supercritical water oxidation treatment of oily
             sludge. In this paper, the supercritical water oxidation process of
             asphaltene was investigated in terms of free radical reaction,
             degradation pathway, and product generation mechanism using ReaxFF
             molecular dynamics simulation method. The results showed that
             increasing temperature, increasing O2, and increasing H2O have
             different effects on HO2{\textperiodcentered}generation. Benzene rings
             undergo fusion and condensation through hydrogenation abstraction and
             oxygen addition reactions, subsequently breaking down into long-chain
             alkanes. Increasing O2 can effectively promote the ring-opening of
             nitrogen-containing heterocycles. -COOH is the most important
             intermediate fragment for CO and CO2 generation, and there is a
             reaction competition with -CHO3 and -CO3. When the number of oxygen
             molecules increases from 300 to 700, the reaction frequency of -CHO3
             and -CO3 to generate CO and CO2 increases by 17.14{~}{\%} and
             12.77{~}{\%}{\textperiodcentered}H2O determines the production of H2
             by controlling the number of H{\textperiodcentered}radicals present.
             As the amount of H2O increases from 500 to 1500, the product ratio of
             H2 increases from 12.73{~}{\%} to 21.31{~}{\%}. ENVIRONMENTAL
             IMPLICATION: Asphaltene is the most structurally complex organic
             matter in oily sludge, and its presence makes it difficult for oily
             sludge to be completely degraded by conventional treatment methods
             such as pyrolysis and incineration. Polycyclic aromatic hydrocarbons
             (PAHs) represented by asphaltene increase the carcinogenicity and
             mutagenicity of oily sludge, and even irreversibly pollute soil and
             groundwater. Supercritical water oxidation, as an efficient organic
             waste treatment technology, can realize harmlessness in a green and
             efficient way. So the study on the mechanism of supercritical water
             oxidation of asphaltene is of great significance for environmental
             protection.},
}

@Article{Yu_Energy_2024_v303_p131990,
    author =   {Xiaozhen Yu and Jihe Chen and Xiangbao Meng and Yujian Zhu and Yadi Li
             and Zhao Qin and Yang Wu and Ke Yan and Shizemin Song},
    title =    {{Polyethylene deflagration characterization and kinetic mechanism
             analysis}},
    journal =  {Energy},
    year =     2024,
    volume =   303,
    pages =    131990,
    doi =      {10.1016/j.energy.2024.131990},
}

@Article{Mao_JPhysDApplPhys_2024_v57_p355501,
    author =   {Yijin Mao and Yuwen Zhang},
    title =    {{Quantifying reaction rates in methane oxidation: atomistic simulations
             at high temperature}},
    journal =  {J. Phys. D: Appl. Phys.},
    year =     2024,
    volume =   57,
    number =   35,
    pages =    355501,
    doi =      {10.1088/1361-6463/ad5217},
    abstract = {{\ensuremath{<}}jats:title{\ensuremath{>}}Abstract{\ensuremath{<}}/jat
             s:title{\ensuremath{>}}
             {\ensuremath{<}}jats:p{\ensuremath{>}}This study presents a
             comprehensive analysis of methane oxidation at high temperatures (2500
             K{\textendash}3500 K){\textemdash}a critical process in atmospheric
             chemistry and energy production. Employing reactive molecular dynamics
             simulations, the research bridges the knowledge gap in understanding
             the complex reaction networks at these elevated temperatures. Key
             features include the identification of intermediate species and the
             simplification of the reaction networks through advanced simulation
             and post-processing techniques. Another focus of the study is on
             employing the Arrhenius equation for nonlinear curve fitting to
             determine activation energy and pre-exponential factors for various
             reactions. The analysis reveals that, despite temperature variations,
             there are 121 common reactions among the reduced reaction systems.
             This discovery revealed the underlying consistency in methane
             oxidation pathways across a range of high temperatures. The results of
             this research are vital for enhancing current models of methane
             oxidation, particularly in the context of improving combustion
             processes and deepening our understanding of atmospheric dynamics
             involving methane.{\ensuremath{<}}/jats:p{\ensuremath{>}}},
}

@Article{Wang_IntJRefrig_2024_v165_p360,
    author =   {Xueyan Wang and Hua Tian and Gequn Shu and Zhao Yang},
    title =    {{Effect of H2O on macroscopic flame behaviors and combustion reaction
             mechanism of 1,1-difluoroethane (R152a)}},
    journal =  {Int. J. Refrig.},
    year =     2024,
    volume =   165,
    pages =    {360--374},
    doi =      {10.1016/j.ijrefrig.2024.05.013},
}

@Article{Schmalz_IntJChemKinet_2024_v56_p501,
    author =   {Felix Schmalz and Wassja A. Kopp and Eirini Goudeli and Kai Leonhard},
    title =    {{Reaction path identification and validation from molecular dynamics
             simulations of hydrocarbon pyrolysis}},
    journal =  {Int J Chem. Kinet.},
    year =     2024,
    volume =   56,
    number =   9,
    pages =    {501--512},
    doi =      {10.1002/kin.21719},
    abstract = {{\ensuremath{<}}jats:title{\ensuremath{>}}Abstract{\ensuremath{<}}/jat
             s:title{\ensuremath{>}}{\ensuremath{<}}jats:p{\ensuremath{>}}Creation
             of complex chemical mechanisms for hydrocarbon pyrolysis and
             combustion is challenging due to the large number of species and
             reactions involved. Reactive molecular dynamics (RMD) enables the
             simulation of thousands of reactions and the discovery of previously
             unknown components of the reaction network. However, due to the
             inherent imprecision of reactive force fields, it is necessary to
             verify RMD{-}obtained reaction paths using more accurate methods such
             as Density Functional Theory (DFT). We demonstrate a method for
             identification and confirmation of reaction pathways from RMD that
             supplement an established mechanism, using the example of benzene
             formation from {\ensuremath{<}}jats:italic{\ensuremath{>}}n{\ensuremat
             h{<}}/jats:italic{\ensuremath{>}}{-}heptane and {\ensuremath{<}}jats:i
             talic{\ensuremath{>}}iso{\ensuremath{<}}/jats:italic{\ensuremath{>}}{-
             }octane pyrolysis. We establish a validation workflow to extract
             reaction geometries from RMD and optimize transition states using the
             Nudged{-}Elastic{-}Band method on semi{-}empirical and quantum
             mechanical levels of theory. Our findings demonstrate that the widely
             recognized ReaxFF parameterization, CHO2016, can identify known
             pathways from a established soot formation mechanism while also
             indicating new ones. We also show that CHO2016 underestimates hydrogen
             migration barriers by up to  as compared to DFT and can lower
             activation barriers significantly for spin{-}forbidden reactions. This
             highlights the necessity for validation or potentially even
             reparametrization of{~}CHO2016.{\ensuremath{<}}/jats:p{\ensuremath{>}}},
}

@Article{Ma_AngewChemInternationalEngl_2024_v63_pe202403614,
    author =   {Lunliang Ma and Tao Wang},
    title =    {{Rational Understanding Hydroxide Diffusion Mechanism in Anion Exchange
             Membranes during Electrochemical Processes with RDAnalyzer}},
    journal =  {Angew. Chem. (International, Engl.)},
    year =     2024,
    volume =   63,
    number =   34,
    pages =    {e202403614},
    doi =      {10.1002/anie.202403614},
    abstract = {Enhancing the understanding of hydroxide transport mechanisms in anion
             exchange membranes (AEMs) is beneficial for the rational design of
             high-performance AEMs in the renewable energy system. However, the
             high complexity and lack of adequate analytic tools make it
             challenging to clarify different mechanisms unambiguously. Herein, we
             developed an in-house toolkit, the Reactive Diffusion Analyzer
             (RDAnalyzer), to conduct an effective analysis of hydroxide diffusion
             mechanisms from ReaxFF molecular dynamic simulations. Using the
             experimentally well-synthesized T20NC6NC5N as a model system, we
             successfully decoupled the hydroxide diffusion mechanisms into free
             Vehicular and free Grotthuss, as well as associated Vehicular and
             associated Grotthuss, which was not yet achieved previously.
             Meanwhile, RDAnalyzer managed to specifically identify the drift
             length of hydroxide species for each mechanism under the electric
             field, which worked as a useful variable for calculating the
             conductivity of AEMs. Our theoretically predicted conductivity for
             T20NC6NC5N agrees reasonably with experimental results, indicating the
             reliability of RDAnalyzer. This work not only provides a rational
             understanding of the complex hydroxide diffusion mechanisms in AEMs
             but also holds the potential to guide the rational design of high-
             performance AEMs with computations.},
}

@Article{Xing_IntJHydrogEnergy_2024_v77_p126,
    author =   {Zhihao Xing and Xi Jiang and Roger F. Cracknell},
    title =    {{Investigation of the chemical mechanism of pollutant formation in co-
             firing of ammonia and biomass lignin}},
    journal =  {Int. J. Hydrog. Energy},
    year =     2024,
    volume =   77,
    pages =    {126--137},
    doi =      {10.1016/j.ijhydene.2024.06.171},
}

@Article{Lv_MaterTodayCommun_2024_v40_p109624,
    author =   {Meiheng Lv and Yifan Zhang and Runze Liu and Yinhua Ma and Li Liu and
             Wenze Li and Huaxin Liu and Jianyong Liu},
    title =    {{Exploring the thermal decomposition mechanism of nitromethane via a
             neural network potential}},
    journal =  {Mater. Today Commun.},
    year =     2024,
    volume =   40,
    pages =    109624,
    doi =      {10.1016/j.mtcomm.2024.109624},
}

@Article{Deng_JEnergyInst_2024_v115_p101676,
    author =   {Bingxin Deng and Xiaoya Chang and Yongjin Wang and Qingzhao Chu and
             Dongping Chen},
    title =    {{Pyrolysis mechanism of a highly branched bio-derived fuel and its
             blends with aviation kerosene (RP-3)}},
    journal =  {J. Energy Inst.},
    year =     2024,
    volume =   115,
    pages =    101676,
    doi =      {10.1016/j.joei.2024.101676},
}

@Article{Wei_Energies_2024_v17_p3536,
    author =   {Yu Wei and Xiaohui Zhang and Shan Qing and Hua Wang},
    title =    {{Reaction Mechanism of Pyrolysis and Combustion of Methyl Oleate: A
             ReaxFF-MD Analysis}},
    journal =  {Energies},
    year =     2024,
    volume =   17,
    number =   14,
    pages =    3536,
    doi =      {10.3390/en17143536},
    abstract = {{\ensuremath{<}}jats:p{\ensuremath{>}}As an emerging environmentally
             friendly fuel, biodiesel has excellent fuel properties comparable to
             those of petrochemical diesel. Oleic acid methyl ester, as the main
             component of biodiesel, has the characteristics of high cetane number
             and low emission rate of harmful gases. However, the comprehensive
             chemical conversion pathway of oleic acid methyl ester is not clear.
             In this paper, the reactive force field molecular dynamics simulation
             (ReaxFF-MD) method is used to construct a model of oleic acid methyl
             ester pyrolysis and combustion system. Further, the chemical
             conversion kinetics process at high temperatures (2500
             K{\textendash}3500 K) was studied, and a chemical reaction network was
             drawn. The research results show that the density of the system has
             almost no effect on the decomposition activation energy of oleic acid
             methyl ester, and the activation energies of its pyrolysis and
             combustion processes are 190.02 kJ/mol and 144.89 kJ/mol,
             respectively. Ethylene, water and carbon dioxide are the dominant and
             most accumulated products. From the specific reaction mechanism, the
             main pyrolysis path of oleic acid methyl ester is the breakage of the
             C-C bond to produce small molecule intermediates, and subsequent
             transformation of the ester group radical into carbon oxides. The
             combustion path is the evolution of long-chain alkanes into short-
             carbon-chain gaseous products, and these species are further burned to
             form stable CO2 and H2O. This study further discusses the microscopic
             combustion kinetics of biodiesel, providing a reference for the
             construction of biodiesel combustion models. Based on this theoretical
             study, the understanding of free radicals, intermediates, and products
             in the pyrolysis and combustion of biomass can be
             deepened.{\ensuremath{<}}/jats:p{\ensuremath{>}}},
}

@Article{Li_SciTotalEnviron_2024_v931_p172921,
    author =   {Haotian Li and Fuping Zeng and Xinnuo Guo and Kexin Zhu and Ju Tang},
    title =    {{Thermal degradation of greenhouse gas SF6 at realistic temperatures:
             Insights from atomic-scale CVHD simulations}},
    journal =  {Sci. Total. Environ.},
    year =     2024,
    volume =   931,
    pages =    172921,
    doi =      {10.1016/j.scitotenv.2024.172921},
    abstract = {Sulfur hexafluoride (SF6), recognized as a potent greenhouse gas with
             significant contributions to climate change, presents challenges in
             understanding its degradation processes. Molecular dynamics
             simulations are valuable tools for understanding modes of
             decomposition while the traditional approaches face limitations in
             time scale and require unrealistically high temperatures. The
             collective variable-driven hyperdynamics (CVHD) approach has been
             introduced to directly depict the pyrolysis process for SF6 gas at
             practical application temperatures, as low as 1600{~}K for the first
             time. Achieving an unprecedented acceleration factor of up to 107, the
             method extends the simulation time scale to milliseconds and beyond
             while maintaining consistency with experimental and theoretical
             models. The differences in the reaction process between simulations
             conducted at actual and elevated temperatures have been noted,
             providing insights into SF6 degradation pathways. The work provides a
             basis for the further studies on the thermal degradation of
             pollutants.},
}

@Article{Xing_ChemEngJ_2024_v489_p151492,
    author =   {Zhihao Xing and Xi Jiang},
    title =    {{Neural network potential-based molecular investigation of pollutant
             formation of ammonia and ammonia-hydrogen combustion}},
    journal =  {Chem. Eng. J.},
    year =     2024,
    volume =   489,
    pages =    151492,
    doi =      {10.1016/j.cej.2024.151492},
}

@Article{Yang_FireSafJ_2024_v146_p104157,
    author =   {Zhihui Yang and Yinan Qiu and Wei Chen},
    title =    {{Inhibition mechanism of CHF3 on hydrogen{\textendash}oxygen
             combustion: Insights from reactive force field molecular dynamics
             simulations}},
    journal =  {Fire Saf. J.},
    year =     2024,
    volume =   146,
    pages =    104157,
    doi =      {10.1016/j.firesaf.2024.104157},
}

@Article{Yang_Energy_2024_v295_p131013,
    author =   {Yu Yang and Reo Kai and Hiroaki Watanabe},
    title =    {{Reaction mechanism and light gas conversion in pyrolysis and oxidation
             of dimethyl ether (DME): A ReaxFF molecular dynamics study}},
    journal =  {Energy},
    year =     2024,
    volume =   295,
    pages =    131013,
    doi =      {10.1016/j.energy.2024.131013},
}

@Article{Cao_JPhysDApplPhys_2024_v57_p195204,
    author =   {Weidong Cao and Xingwen Li and Yanfeng Zhang and Qian Wang and Renjie
             Yu and Zhenyi Chen and Tao Zhuang},
    title =    {{Atomic-scale insight into arc plasma radiation-induced gassing
             materials ablation: photothermal decomposition behavior}},
    journal =  {J. Phys. D: Appl. Phys.},
    year =     2024,
    volume =   57,
    number =   19,
    pages =    195204,
    doi =      {10.1088/1361-6463/ad2562},
    abstract = {{\ensuremath{<}}jats:title{\ensuremath{>}}Abstract{\ensuremath{<}}/jat
             s:title{\ensuremath{>}}
             {\ensuremath{<}}jats:p{\ensuremath{>}}In this study, we present a
             novel computational atomistic study of the photothermal decomposition
             behavior of arc plasma on radiation-induced gassing materials
             ablation, studying a polyamide 66 (PA66) system using reactive force
             field (ReaxFF) molecular dynamics (MD). We determine the infrared (IR)
             vibrational frequency of the PA66 permanent molecular dipole using MD
             and then computationally impose an electric field at the same
             frequency to simulate photothermal decomposition by IR, verifying our
             observations with gas chromatography-mass spectrometry (GCMS) of
             experimental decomposition. MD indicates that photothermal
             decomposition reaction is dominated by either cleavage at low
             temperature or cyclization at high temperature. At low temperature,
             initial chain scission takes place at the two amide C{\textendash}N,
             and the remaining chains break down into a variety of molecular
             fragments and free radicals. Further increasing the temperature
             stabilizes a variety of branched chain structures via cyclization,
             debranching and polymerization, with further cleavage forming
             hydrocarbons and volatile small molecule gases. Overall, H{\ensuremath
             {<}}jats:sub{\ensuremath{>}}2{\ensuremath{<}}/jats:sub{\ensuremath{>}}
             , CO, H{\ensuremath{<}}jats:sub{\ensuremath{>}}2{\ensuremath{<}}/jats:
             sub{\ensuremath{>}}O, alkanes and alkenes are the main gaseous
             products and cyclic structures (especially nitrogen-containing three-
             membered ring) are the main solid products during the photothermal
             decomposition of PA66, and their formation results from a variety of
             complex chemical reactions. The results of MD cover the experimental
             observations of GCMS, demonstrating that this computational
             methodology helps us understand the molecular breakdown mechanisms of
             arc plasma radiation-induced gassing materials. We also discuss the
             physical mechanism by which the main gas can accelerate arc quenching,
             and the importance and necessity of using electric fields to simulate
             IR photothermal decomposition of arc-induced
             ablation.{\ensuremath{<}}/jats:p{\ensuremath{>}}},
}

@Article{Xiao_BioresourTechnol_2024_v399_p130590,
    author =   {Yuqin Xiao and Yuxin Yan and Hainam Do and Richard Rankin and Haitao
             Zhao and Ping Qian and Keke Song and Tao Wu and Cheng Heng Pang},
    title =    {{Understanding cellulose pyrolysis via ab initio deep learning
             potential field}},
    journal =  {Bioresour. Technol.},
    year =     2024,
    volume =   399,
    pages =    130590,
    doi =      {10.1016/j.biortech.2024.130590},
    abstract = {Comprehensive and dynamic studies of cellulose pyrolysis reaction
             mechanisms are crucial in designing experiments and processes with
             enhanced safety, efficiency, and sustainability. The details of the
             pyrolysis mechanism are not readily available from experiments but can
             be better described via molecular dynamics (MD) simulations. However,
             the large size of cellulose molecules challenges accurate ab initio MD
             simulations, while existing reactive force field parameters lack
             precision. In this work, precise ab initio deep learning potentials
             field (DPLF) are developed and applied in MD simulations to facilitate
             the study of cellulose pyrolysis mechanisms. The formation mechanism
             and production rate of both valuable and greenhouse products from
             cellulose at temperatures larger than 1073{~}K are comprehensively
             described. This study underscores the critical role of advanced
             simulation techniques, particularly DLPF, in achieving efficient and
             accurate understanding of cellulose pyrolysis mechanisms, thus
             promoting wider industrial applications.},
}

@Article{Guo_AcsCatal_2024_v14_p5720,
    author =   {Hui Guo and Hong Zhu and Gao-Yong Liu and Zhao-Xu Chen},
    title =    {{General Reaction Network Exploration Scheme Based on Graph Theory
             Representation and Depth First Search Applied to CO<sub>2</sub> Hydrogenation on
             Pd<sub>2</sub>Cu Catalyst}},
    journal =  {Acs Catal.},
    year =     2024,
    volume =   14,
    number =   8,
    pages =    {5720--5734},
    doi =      {10.1021/acscatal.4c00067},
}

@Article{She_IntJHydrogEnergy_2024_v63_p1197,
    author =   {Chongchong She and Manman Wang and Jiaming Gao and Zhi Wang and
             Shaohua Jin and Minglei Chen and Liang Song and Pengwan Chen and Kun
             Chen},
    title =    {{Study on combustion mechanism of methanol/nitromethane based on
             reactive molecular dynamics simulation}},
    journal =  {Int. J. Hydrog. Energy},
    year =     2024,
    volume =   63,
    pages =    {1197--1211},
    doi =      {10.1016/j.ijhydene.2024.03.185},
}

@Article{Liu_ChemEngSci_2024_v287_p119709,
    author =   {Chunjing Liu and Dikun Hong and Wenchang Zhao and Fei Zheng and Weiran
             Lyu and Jianyi Lu},
    title =    {{Transformation simulation of N-containing functional groups in coal
             pyrolysis and combustion processes by using ReaxFF}},
    journal =  {Chem. Eng. Sci.},
    year =     2024,
    volume =   287,
    pages =    119709,
    doi =      {10.1016/j.ces.2024.119709},
}

@Article{Ruan_TribolInt_2024_v192_p109291,
    author =   {Xiaopeng Ruan and Xiaomei Wang and Rui Zhou and Yang Zhao and Luyao
             Bao and Feng Zhou and Zhibin Lu},
    title =    {{Dynamic chemisorption and tribochemistry of
             {\ensuremath{\alpha}}-lipoic-acid-ester on ferrous surfaces}},
    journal =  {Tribol. Int.},
    year =     2024,
    volume =   192,
    pages =    109291,
    doi =      {10.1016/j.triboint.2024.109291},
}

@Article{Wang_Fuel_2024_v361_p130522,
    author =   {Huijuan Wang and Wei Xia and Huimin Yu and Hua Chen and Yongli Pan and
             Yingxin Sun and Shengtao Li and Sheng Han},
    title =    {{A theoretical investigation on the transformer oil pyrolysis mechanism
             and the effect of the small molecule acid in oils}},
    journal =  {Fuel},
    year =     2024,
    volume =   361,
    pages =    130522,
    doi =      {10.1016/j.fuel.2023.130522},
}

@Article{Xiao_PhysChemChemPhys_2024_v26_p11867,
    author =   {Hang Xiao and Bin Yang},
    title =    {{A neural network potential energy surface assisted molecular dynamics
             study on the pyrolysis behavior of two spiro-hydrocarbons}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   15,
    pages =    {11867--11879},
    doi =      {10.1039/d3cp05425j},
    abstract = {Spiro-hydrocarbons are potentially a type of novel alternative jet
             fuel due to their high density and net heat of combustion. In this
             work, the pyrolysis study of two spiro-hydrocarbons
             (spiro[cyclopropane-1,6'-tricyclo[3.2.1.02,4]octane] (C10H14) as Fuel
             1 and spiro[bicyclo[2.2.1]heptane-2,1'-cyclopropane] (C9H14) as Fuel
             2) is performed via molecular dynamics (MD) simulations, with a neural
             network potential energy surface (NNPES), deep potential (DP) model,
             adopted. The data set for the DP model of each fuel is constructed
             after 31 and 27 iterations, respectively. The high precision of the DP
             model is demonstrated, and the temperature transferability of each
             model is observed. The overall pyrolysis performance is evaluated with
             the fuel decomposition rate, showing that both fuels have comparable
             gas-reactivity to commercial aviation fuels, such as JP-10. The
             reaction networks of initial pyrolysis for Fuels 1 and 2 are
             constructed, and the contribution of each pathway is discussed. Fuel 1
             tends to form an unsaturated six-membered ring structure, while Fuel 2
             generates unsaturated open-chain hydrocarbons. Further analyses of the
             MD results provide time-evolution information on each component in the
             pyrolysis species pool. Compared to Fuel 1, the initial pyrolysis of
             Fuel 2 leads to more hydrogen, alkenes, and alkanes, as well as fewer
             monocyclic aromatic hydrocarbons (MAHs), demonstrating a reduced
             tendency for afterward coking. This work might contribute to the
             development of the mechanism of the two spiro-hydrocarbons and guide
             the research of other similar structural fuels.},
}

@Article{Pang_PhysChemChemPhys_2024_v26_p11545,
    author =   {Kehui Pang and Mingjie Wen and Xiaoya Chang and Yabei Xu and Qingzhao
             Chu and Dongping Chen},
    title =    {{The thermal decomposition mechanism of RDX/AP composites: ab initio
             neural network MD simulations}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   15,
    pages =    {11545--11557},
    doi =      {10.1039/d3cp05709g},
    abstract = {A neural network potential (NNP) is developed to investigate the
             decomposition mechanism of RDX, AP, and their composites. Utilizing an
             ab initio dataset, the NNP is evaluated in terms of atomic energy and
             forces, demonstrating strong agreement with ab initio calculations.
             Numerical stability tests across a range of timesteps reveal excellent
             stability compared to the state-of-the-art ReaxFF models. Then the
             thermal decomposition of pure RDX, AP, and RDX/AP composites is
             performed using NNP to explore the coupling effect between RDX and AP.
             The results highlight a dual interaction between RDX and AP, i.e., AP
             accelerates RDX decomposition, particularly at low temperatures, and
             RDX promotes AP decomposition. Analyzing RDX trajectories at the
             RDX/AP interface unveils a three-part decomposition mechanism
             involving N-N bond cleavage, H transfer with AP to form Cl-containing
             acid, and chain-breaking reactions generating small molecules such as
             N2, CO, and CO2. The presence of AP enhances H transfer reactions,
             contributing to its role in promoting RDX decomposition. This work
             studies the reaction kinetics of RDX/AP composites from the atomic
             point of view, and can be widely used in the establishment of reaction
             kinetics models of composite systems with energetic materials.},
}

@Article{Liu_JPhysChemA_2024_v128_p1656,
    author =   {Ziyi Liu and An-Hui Lu and Dongqi Wang},
    title =    {{Deep Potential Molecular Dynamics Study of Propane Oxidative
             Dehydrogenation}},
    journal =  {J. Phys. Chem., A},
    year =     2024,
    volume =   128,
    number =   9,
    pages =    {1656--1664},
    doi =      {10.1021/acs.jpca.3c07859},
    abstract = {Oxidative dehydrogenation (ODH) of light alkanes is a key process in
             the oxidative conversion of alkanes to alkenes, oxygenated
             hydrocarbons, and COx (x = 1,2). Understanding the underlying
             mechanisms extensively is crucial to keep the ODH under control for
             target products, e.g., alkenes rather than COx, with minimal energy
             consumption, e.g., during the alkene production or maximal energy
             release, e.g., during combustion. In this work, deep potential (DP), a
             neural network atomic potential developed in recent years, was
             employed to conduct large-scale accurate reactive dynamic simulations.
             The model was trained on a sufficient data set obtained at the density
             functional theory level. The intricate reaction network was elucidated
             and organized in the form of a hierarchical network to demonstrate the
             key features of the ODH mechanisms, including the activation of
             propane and oxygen, the influence of propyl reaction pathways on the
             propene selectivity, and the role of rapid H2O2 decomposition for
             sustainable and efficient ODH reactions. The results indicate the more
             complex reaction mechanism of propane ODH than that of ethane ODH and
             are expected to provide insights in the ODH catalyst optimization. In
             addition, this work represents the first application of deep potential
             in the ODH mechanistic study and demonstrates the ample advantages of
             DP in the study of mechanism and dynamics of complex systems.},
}

@Article{Wang_IndEngChemRes_2024_v63_p3554,
    author =   {Ruikun Wang and Hongbao Zhang and Dikun Hong and Shiteng Tan and
             Zhenghui Zhao and Lichao Ge},
    title =    {{Interphase Migration of Nitrogen and the Evolution Mechanism of
             Nitrogen-Containing Functional Groups in Char during Sludge Pyrolysis}},
    journal =  {Ind. Eng. Chem. Res.},
    year =     2024,
    volume =   63,
    number =   8,
    pages =    {3554--3562},
    doi =      {10.1021/acs.iecr.3c04052},
}

@Article{Jiang_JMolLiq_2024_v396_p124040,
    author =   {Jun Jiang and Si-Yu Xu and Feng-Qi Zhao and Xue-Hai Ju},
    title =    {{New insights into the degradation mechanism of TNT in supercritical
             water: Combining density functional theory with the reactive force
             field}},
    journal =  {J. Mol. Liq.},
    year =     2024,
    volume =   396,
    pages =    124040,
    doi =      {10.1016/j.molliq.2024.124040},
}

@Article{Li_ChemEngSci_2024_v284_p119528,
    author =   {Jifan Li and Xiaohui Zhang and Aimin Zhang and Hua Wang},
    title =    {{ReaxFF based molecular dynamics simulation of ethyl butyrate in
             pyrolysis and combustion}},
    journal =  {Chem. Eng. Sci.},
    year =     2024,
    volume =   284,
    pages =    119528,
    doi =      {10.1016/j.ces.2023.119528},
}

@Article{Xiao_ProcCombustInst_2024_v40_p105525,
    author =   {Hang Xiao and Zhaohan Chu and Changyang Wang and Jinghui Lu and Long
             Zhao and Bin Yang},
    title =    {{Revealing the initial pyrolysis behavior of decalin in an experimental
             study coupled with neural network-assisted molecular dynamics}},
    journal =  {Proc. Combust. Inst.},
    year =     2024,
    volume =   40,
    number =   {1-4},
    pages =    105525,
    doi =      {10.1016/j.proci.2024.105525},
}

@Article{Wen_PhysChemChemPhys_2024_v26_p9984,
    author =   {Mingjie Wen and Xiaoya Chang and Yabei Xu and Dongping Chen and
             Qingzhao Chu},
    title =    {{Determining the mechanical and decomposition properties of high
             energetic materials ({\ensuremath{\alpha}}-RDX,
             {\ensuremath{\beta}}-HMX, and {\ensuremath{\varepsilon}}-CL-20) using
             a neural network potential}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2024,
    volume =   26,
    number =   13,
    pages =    {9984--9997},
    doi =      {10.1039/d4cp00017j},
    abstract = {Molecular simulations of high energetic materials (HEMs) are limited
             by efficiency and accuracy. Recently, neural network potential (NNP)
             models have achieved molecular simulations of millions of atoms while
             maintaining the accuracy of density functional theory (DFT) levels.
             Herein, an NNP model covering typical HEMs containing C, H, N, and O
             elements is developed. The mechanical and decomposition properties of
             1,3,5-trinitroperhydro-1,3,5-triazine (RDX),
             hexahydro-1,3,5-trinitro-1,3,5-triazine (HMX), and
             2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) are determined by
             employing the molecular dynamics (MD) simulations based on the NNP
             model. The calculated results show that the mechanical properties of
             {\ensuremath{\alpha}}-RDX, {\ensuremath{\beta}}-HMX, and
             {\ensuremath{\varepsilon}}-CL-20 agree with previous experiments and
             theoretical results, including cell parameters, equations of state,
             and elastic constants. In the thermal decomposition simulations, it is
             also found that the initial decomposition reactions of the three
             crystals are N-NO2 homolysis, corresponding radical intermediates
             formation, and NO2-induced reactions. This decomposition trajectory is
             mainly divided into two stages separating from the peak of NO2:
             pyrolysis and oxidation. Overall, the NNP model for C/H/N/O elements
             in this work is an alternative reactive force field for RDX, HMX, and
             CL-20 HEMs, and it opens up new potential for future kinetic study of
             nitramine explosives.},
}

@Article{Sun_MolBaselSwitz_2023_v29_p56,
    author =   {Zijian Sun and Jincheng Ji and Weihua Zhu},
    title =    {{Effects of Nanoparticle Size on the Thermal Decomposition Mechanisms
             of 3,5-Diamino-6-hydroxy-2-oxide-4-nitropyrimidone through ReaxFF
             Large-Scale Molecular Dynamics Simulations}},
    journal =  {Mol. (Basel Switz.)},
    year =     2023,
    volume =   29,
    number =   1,
    pages =    56,
    doi =      {10.3390/molecules29010056},
    abstract = {ReaxFF-lg molecular dynamics method was employed to simulate the
             decomposition processes of IHEM-1 nanoparticles at high temperatures.
             The findings indicate that the initial decomposition paths of the
             nanoparticles with different sizes at varying temperatures are
             similar, where the bimolecular polymerization reaction occurred first.
             Particle size has little effect on the initial decomposition pathway,
             whereas there are differences in the numbers of the species during the
             decomposition and their evolution trends. The formation of the
             hydroxyl radicals is the dominant decomposition mechanism with the
             highest reaction frequency. The degradation rate of the IHEM-1
             molecules gradually increases with the increasing temperature. The
             IHEM-1 nanoparticles with smaller sizes exhibit greater decomposition
             rate constants. The activation energies for the decomposition are
             lower than the reported experimental values of bulk explosives, which
             suggests a higher sensitivity.},
}

@Article{Sun_ComputTheorChem_2024_v1231_p114446,
    author =   {Haoshan Sun and Xiaohui Zhang and Hongxi Liu and Jifan Li and Hua Wang},
    title =    {{Pyrolysis and combustion reaction mechanisms of methyl palmitate with
             ReaxFF-MD method}},
    journal =  {Comput. Theor. Chem.},
    year =     2024,
    volume =   1231,
    pages =    114446,
    doi =      {10.1016/j.comptc.2023.114446},
}

@Article{Li_IntJGreenEnergy_2024_v21_p2117,
    author =   {Yunlong Li and Yinan Qiu and Zheng Wang and Wei Chen},
    title =    {{Molecular dynamics simulation of the inhibition effects of inert gases
             (Ar/He/N<sub>2</sub>}             ) on hydrogen oxidation}},
    journal =  {Int. J. Green Energy},
    year =     2024,
    volume =   21,
    number =   9,
    pages =    {2117--2127},
    doi =      {10.1080/15435075.2023.2297766},
    abstract = {ABSTRACT In this paper, the inhibition effects of Ar/He/N2 on the
             H2-O2 system near the extended second explosion limit were
             investigated by ReaxFF simulations. It was found that all three inert
             gases can inhibit the reactions, delaying the initiation reaction, and
             prolonging ignition delay since the generation and consumption of free
             radicals such as HO2 and OH were suppressed. Further, the inhibitory
             effect has the correlation of Ar {\ensuremath{>}} He {\ensuremath{>}}
             N2. The inhibitory effect of Ar is more pronounced compared to He due
             to the bigger effective radius and physical mass. Moreover, the
             addition of N2 introduced extra initiation reaction (H2{\,}+{\,}N2
             {\textrightarrow} NNH{\,}+{\,}H) and generated additional intermediate
             products such as NNH and N2OH, which result in the weakest inhibitory
             effect. Compared to diatomic inhibitors (e.g., N2), the monatomic
             inhibitors such as Ar and He exhibit stronger inhibitory effects on
             the hydroxide reaction under high pressure.},
}

@Article{She_Fuel_2025_v379_p132982,
    author =   {Chongchong She and Tiancheng Zhang and Jiaming Gao and Zhi Wang and
             Shaohua Jin and Lijie Li and Junfeng Wang and Liang Song and Pengwan
             Chen and Kun Chen},
    title =    {{Insights into the combustion mechanisms of turpentine oil based on
             ReaxFF molecular dynamics simulations}},
    journal =  {Fuel},
    year =     2025,
    volume =   379,
    pages =    132982,
    doi =      {10.1016/j.fuel.2024.132982},
}
@Article{Mao_ComputPhysCommun_2023_v291_p108842,
    author =   {Runze Mao and Minqi Lin and Yan Zhang and Tianhan Zhang and Zhi-Qin
             John Xu and Zhi X. Chen},
    title =    {{DeepFlame: A deep learning empowered open-source platform for reacting
             flow simulations}},
    journal =  {Comput. Phys. Commun.},
    year =     2023,
    volume =   291,
    pages =    108842,
    doi =      {10.1016/j.cpc.2023.108842},
}

@Article{Chen_PhysFluids_2024_v36,
    author =   {Huangwei Chen and MingHao Zhao and Hua Qiu and Yuejin Zhu},
    title =    {{Implementation and verification of an OpenFOAM solver for gas-droplet
             two-phase detonation combustion}},
    journal =  {Phys. Fluids},
    year =     2024,
    volume =   36,
    number =   8,
    doi =      {10.1063/5.0221308},
    abstract = {Due to the complexity and short timescale of detonation, it is usually
             difficult to capture its transient characteristics experimentally.
             Advanced numerical methods are essential for enhancing the
             understanding of the flow field structure and combustion mechanism of
             detonation. In this study, a density-based compressible reactive flow
             solver called CDSFoam is developed for simulating gas-droplet two-
             phase detonation combustion based on OpenFOAM. The primary feature of
             this solver is its implementation of two-way coupling between gas and
             liquid phases, utilizing the Eulerian{\textendash}Lagrangian method.
             The key enhancement is an improved approximate Riemann solver used to
             solve the convective flux, reducing dissipation while ensuring
             robustness. Time integration is achieved through the third-order
             strong stability preserving Runge{\textendash}Kutta method.
             Additionally, CDSFoam incorporates dynamic load balancing and adaptive
             mesh refinement techniques to mitigate computational costs while
             achieving high-resolution flow fields dynamically. To validate the
             reliability and accuracy of the solver, a series of benchmark cases
             are examined, including the multi-component inert and reactive shock
             tube, the stable diffusion process, the Riemann problem, the one-
             dimensional detonation, the two-dimensional detonation and oblique
             detonation, the droplet phase model, the two-dimensional
             gas{\textendash}liquid two-phase detonation, and the two-phase
             rotating detonation. The results show that CDSFoam can well predict
             the shock wave discontinuity, shock wave induced ignition, molecular
             diffusion, detonation key parameters, detonation cell size, and the
             main characteristics of gas{\textendash}liquid two-phase detonation.},
}

@Article{Zhang_PhysFluids_2024_v36,
    author =   {Min Zhang and Runze Mao and Han Li and Zhenhua An and Zhi X. Chen},
    title =    {{Graphics processing unit/artificial neural network-accelerated large-
             eddy simulation of swirling premixed flames}},
    journal =  {Phys. Fluids},
    year =     2024,
    volume =   36,
    number =   5,
    doi =      {10.1063/5.0202321},
    abstract = {Within the scope of reacting flow simulations, the real-time direct
             integration (DI) of stiff ordinary differential equations for the
             computation of chemical kinetics stands as the primary demand on
             computational resources. Meanwhile, as the number of transport
             equations that need to be solved increases, the computational cost
             grows more substantially, particularly for those combustion models
             involving direct coupling of chemistry and flow such as the
             transported probability density function model. In the current study,
             an integrated graphics processing unit-artificial neural network (GPU-
             ANN) framework is introduced to comply with heavy computational costs
             while maintaining high fidelity. Within this framework, a GPU-based
             solver is employed to solve partial differential equations and compute
             thermal and transport properties, and an ANN is utilized to replace
             the calculation of reaction rates. Large eddy simulations of two
             swirling flames provide a robust validation, affirming and extending
             the GPU-ANN approach's applicability to challenging scenarios. The
             simulation results demonstrate a strong correlation in the macro flame
             structure and statistical characteristics between the GPU-ANN approach
             and the traditional central processing unit (CPU)-based solver with
             DI. This comparison indicates that the GPU-ANN approach is capable of
             attaining the same degree of precision as the conventional CPU-DI
             solver, even in more complex scenarios. In addition, the overall
             speed-up factor for the GPU-ANN approach is over two orders of
             magnitude. This study establishes the potential groundwork for
             widespread application of the proposed GPU-ANN approach in combustion
             simulations, addressing various and complex scenarios based on
             detailed chemistry, while significantly reducing computational costs.},
}
@Article{Wang_NatCommun_2024_v15_p1904,
    author =   {Jingqi Wang and Jiapeng Liu and Hongshuai Wang and Musen Zhou and
             Guolin Ke and Linfeng Zhang and Jianzhong Wu and Zhifeng Gao and
             Diannan Lu},
    title =    {{A comprehensive transformer-based approach for high-accuracy gas
             adsorption predictions in metal-organic frameworks}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    1904,
    doi =      {10.1038/s41467-024-46276-x},
    abstract = {Gas separation is crucial for industrial production and environmental
             protection, with metal-organic frameworks (MOFs) offering a promising
             solution due to their tunable structural properties and chemical
             compositions. Traditional simulation approaches, such as molecular
             dynamics, are complex and computationally demanding. Although feature
             engineering-based machine learning methods perform better, they are
             susceptible to overfitting because of limited labeled data.
             Furthermore, these methods are typically designed for single tasks,
             such as predicting gas adsorption capacity under specific conditions,
             which restricts the utilization of comprehensive datasets including
             all adsorption capacities. To address these challenges, we propose
             Uni-MOF, an innovative framework for large-scale, three-dimensional
             MOF representation learning, designed for multi-purpose gas
             prediction. Specifically, Uni-MOF serves as a versatile gas adsorption
             estimator for MOF materials, employing pure three-dimensional
             representations learned from over 631,000 collected MOF and COF
             structures. Our experimental results show that Uni-MOF can
             automatically extract structural representations and predict
             adsorption capacities under various operating conditions using a
             single model. For simulated data, Uni-MOF exhibits remarkably high
             predictive accuracy across all datasets. Additionally, the values
             predicted by Uni-MOF correspond with the outcomes of adsorption
             experiments. Furthermore, Uni-MOF demonstrates considerable potential
             for broad applicability in predicting a wide array of other
             properties.},
}


@Article{Luo_JacsAu_2024_v4_p3451,
    author =   {Weiliang Luo and Gengmo Zhou and Zhengdan Zhu and Yannan Yuan and
             Guolin Ke and Zhewei Wei and Zhifeng Gao and Hang Zheng},
    title =    {{Bridging Machine Learning and Thermodynamics for Accurate pK a
             Prediction}},
    journal =  {Jacs Au},
    year =     2024,
    volume =   4,
    number =   9,
    pages =    {3451--3465},
    doi =      {10.1021/jacsau.4c00271},
    abstract = {Integrating scientific principles into machine learning models to
             enhance their predictive performance and generalizability is a central
             challenge in the development of AI for Science. Herein, we introduce
             Uni-pK a, a novel framework that successfully incorporates
             thermodynamic principles into machine learning modeling, achieving
             high-precision predictions of acid dissociation constants (pK a), a
             crucial task in the rational design of drugs and catalysts, as well as
             a modeling challenge in computational physical chemistry for small
             organic molecules. Uni-pK a utilizes a comprehensive free energy model
             to represent molecular protonation equilibria accurately. It features
             a structure enumerator that reconstructs molecular configurations from
             pK a data, coupled with a neural network that functions as a free
             energy predictor, ensuring high-throughput, data-driven prediction
             while preserving thermodynamic consistency. Employing a pretraining-
             finetuning strategy with both predicted and experimental pK a data,
             Uni-pK a not only achieves state-of-the-art accuracy in
             chemoinformatics but also shows comparable precision to quantum
             mechanics-based methods.},
}

@Article{Fan_JChemInfModel_2024_v64_p8414,
    author =   {Jiahao Fan and Ziyao Li and Eric Alcaide and Guolin Ke and Huaqing
             Huang and Weinan E},
    title =    {{Accurate Conformation Sampling via Protein Structural Diffusion}},
    journal =  {J. Chem. Inf. Model.},
    year =     2024,
    volume =   64,
    number =   22,
    pages =    {8414--8426},
    doi =      {10.1021/acs.jcim.4c00928},
    abstract = {Accurate sampling of protein conformations is pivotal for advances in
             biology and medicine. Although there has been tremendous progress in
             protein structure prediction in recent years due to deep learning,
             models that can predict the different stable conformations of proteins
             with high accuracy and structural validity are still lacking. Here, we
             introduce UFConf, a cutting-edge approach designed for robust sampling
             of diverse protein conformations based solely on amino acid sequences.
             This method transforms AlphaFold2 into a diffusion model by
             implementing a conformation-based diffusion process and adapting the
             architecture to process diffused inputs effectively. To counteract the
             inherent conformational bias in the Protein Data Bank, we developed a
             novel hierarchical reweighting protocol based on structural
             clustering. Our evaluations demonstrate that UFConf outperforms
             existing methods in terms of successful sampling and structural
             validity. The comparisons with long-time molecular dynamics show that
             UFConf can overcome the energy barrier existing in molecular dynamics
             simulations and perform more efficient sampling. Furthermore, We
             showcase UFConf's utility in drug discovery through its application in
             neural protein-ligand docking. In a blind test, it accurately
             predicted a novel protein-ligand complex, underscoring its potential
             to impact real-world biological research. Additionally, we present
             other modes of sampling using UFConf, including partial sampling with
             fixed motif, Langevin dynamics, and structural interpolation.},
}

@Article{He_NatCommun_2024_v15_p5163,
    author =   {Xinheng He and Lifen Zhao and Yinping Tian and Rui Li and Qinyu Chu
             and Zhiyong Gu and Mingyue Zheng and Yusong Wang and Shaoning Li and
             Hualiang Jiang and Yi Jiang and Liuqing Wen and Dingyan Wang and Xi
             Cheng},
    title =    {{Highly accurate carbohydrate-binding site prediction with
             DeepGlycanSite}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    5163,
    doi =      {10.1038/s41467-024-49516-2},
    abstract = {As the most abundant organic substances in nature, carbohydrates are
             essential for life. Understanding how carbohydrates regulate proteins
             in the physiological and pathological processes presents opportunities
             to address crucial biological problems and develop new therapeutics.
             However, the diversity and complexity of carbohydrates pose a
             challenge in experimentally identifying the sites where carbohydrates
             bind to and act on proteins. Here, we introduce a deep learning model,
             DeepGlycanSite, capable of accurately predicting carbohydrate-binding
             sites on a given protein structure. Incorporating geometric and
             evolutionary features of proteins into a deep equivariant graph neural
             network with the transformer architecture, DeepGlycanSite remarkably
             outperforms previous state-of-the-art methods and effectively predicts
             binding sites for diverse carbohydrates. Integrating with a
             mutagenesis study, DeepGlycanSite reveals the
             guanosine-5'-diphosphate-sugar-recognition site of an important
             G-protein coupled receptor. These findings demonstrate DeepGlycanSite
             is invaluable for carbohydrate-binding site prediction and could
             provide insights into molecular mechanisms underlying carbohydrate-
             regulation of therapeutically important proteins.},
}

@Article{Comajuncosa-Creus_JCheminformatics_2024_v16_p70,
    author =   {Arnau Comajuncosa-Creus and Aksel Lenes and Miguel S{\'a}nchez-
             Palomino and Dylan Dalton and Patrick Aloy},
    title =    {{Stereochemically-aware bioactivity descriptors for uncharacterized
             chemical compounds}},
    journal =  {J. Cheminformatics},
    year =     2024,
    volume =   16,
    number =   1,
    pages =    70,
    doi =      {10.1186/s13321-024-00867-4},
    abstract = {Stereochemistry plays a fundamental role in pharmacology. Here, we
             systematically investigate the relationship between stereoisomerism
             and bioactivity on over 1{~}M compounds, finding that a very
             significant fraction ({\textasciitilde}{\,}40{\%}) of spatial isomer
             pairs show, to some extent, distinct bioactivities. We then use the 3D
             representation of these molecules to train a collection of deep neural
             networks (Signaturizers3D) to generate bioactivity descriptors
             associated to small molecules, that capture their effects at
             increasing levels of biological complexity (i.e. from protein targets
             to clinical outcomes). Further, we assess the ability of the
             descriptors to distinguish between stereoisomers and to recapitulate
             their different target binding profiles. Overall, we show how these
             new stereochemically-aware descriptors provide an even more faithful
             description of complex small molecule bioactivity properties,
             capturing key differences in the activity of stereoisomers.Scientific
             contributionWe systematically assess the relationship between
             stereoisomerism and bioactivity on a large scale, focusing on
             compound-target binding events, and use our findings to train novel
             deep learning models to generate stereochemically-aware bioactivity
             signatures for any compound of interest.},
}

@Article{Lu_NatCommun_2024_v15_p7104,
    author =   {Shuqi Lu and Zhifeng Gao and Di He and Linfeng Zhang and Guolin Ke},
    title =    {{Data-driven quantum chemical property prediction leveraging 3D
             conformations with Uni-Mol}},
    journal =  {Nat. Commun.},
    year =     2024,
    volume =   15,
    number =   1,
    pages =    7104,
    doi =      {10.1038/s41467-024-51321-w},
    abstract = {Quantum chemical (QC) property prediction is crucial for computational
             materials and drug design, but relies on expensive electronic
             structure calculations like density functional theory (DFT). Recent
             deep learning methods accelerate this process using 1D SMILES or 2D
             graphs as inputs but struggle to achieve high accuracy as most QC
             properties depend on refined 3D molecular equilibrium conformations.
             We introduce Uni-Mol+, a deep learning approach that leverages 3D
             conformations for accurate QC property prediction. Uni-Mol+ first
             generates a raw 3D conformation using RDKit then iteratively refines
             it towards DFT equilibrium conformation using neural networks, which
             is finally used to predict the QC properties. To effectively learn
             this conformation update process, we introduce a two-track Transformer
             model backbone and a novel training approach. Our benchmarking results
             demonstrate that the proposed Uni-Mol+ significantly improves the
             accuracy of QC property prediction in various datasets.},
}


@Article{Ding_JChemInfModel_2024_v64_p2955,
    author =   {Yuheng Ding and Bo Qiang and Qixuan Chen and Yiqiao Liu and Liangren
             Zhang and Zhenming Liu},
    title =    {{Exploring Chemical Reaction Space with Machine Learning Models:
             Representation and Feature Perspective}},
    journal =  {J. Chem. Inf. Model.},
    year =     2024,
    volume =   64,
    number =   8,
    pages =    {2955--2970},
    doi =      {10.1021/acs.jcim.4c00004},
    abstract = {Chemical reactions serve as foundational building blocks for organic
             chemistry and drug design. In the era of large AI models, data-driven
             approaches have emerged to innovate the design of novel reactions,
             optimize existing ones for higher yields, and discover new pathways
             for synthesizing chemical structures comprehensively. To effectively
             address these challenges with machine learning models, it is
             imperative to derive robust and informative representations or engage
             in feature engineering using extensive data sets of reactions. This
             work aims to provide a comprehensive review of established reaction
             featurization approaches, offering insights into the selection of
             representations and the design of features for a wide array of tasks.
             The advantages and limitations of employing SMILES, molecular
             fingerprints, molecular graphs, and physics-based properties are
             meticulously elaborated. Solutions to bridge the gap between different
             representations will also be critically evaluated. Additionally, we
             introduce a new frontier in chemical reaction pretraining, holding
             promise as an innovative yet unexplored avenue.},
}

@Article{Cui_NatMachIntell_2024_v6_p428,
    author =   {Taoyong Cui and Chenyu Tang and Mao Su and Shufei Zhang and Yuqiang Li
             and Lei Bai and Yuhan Dong and Xingao Gong and Wanli Ouyang},
    title =    {{Geometry-enhanced pretraining on interatomic potentials}},
    journal =  {Nat Mach Intell},
    year =     2024,
    volume =   6,
    number =   4,
    pages =    {428--436},
    doi =      {10.1038/s42256-024-00818-6},
}
